In this episode, we discuss how a talented team of researchers from Stanford university have created a robotic platform capable of imitation learning for conducting over 850 common tasks, including searing shrimp!

Podcast: Did A Shrimp Fry This Rice? No, A Robot Did

Robotic systems automate repetitive tasks, handling complex, powerful motions and working in environments that are dangerous or harmful to humans. More integrated, higher-performance microcontrollers (MCUs) enable higher power efficiency and smoother and safer motion with much higher accuracy, therefore increasing productivity and automation. For example, higher accuracy – sometimes within 0.1 mm – is important for applications that handle laser welding, precision coatings, or inkjet or 3D printing.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1717222891211-GUID-20240130-SS0I-FNRF-GSFH-MVQTTTVX1WBR-low.jpg" style="width: 100%px;" class="fr-fic fr-dib">The number of axes in a robot arm and the type of control architecture needed (either centralized or distributed, also known as decentralized) will ultimately determine the right MCU or motor-control integrated circuit (IC) for that system. Modern factories use a mix of robots with varying numbers of axes, defined as the degree of movement and rotation across x, y or z planes, in order to handle different phases of manufacturing; therefore, there are various control architectures throughout the factory floor.It’s important to choose MCUs with additional performance headroom to provide future scalability, as well as the capacity to support add-on features. Planning for scalability and additional features can reduce cost, time and complexity during the design process.In this article, I’ll explore centralized and distributed motor-control architectures and the design considerations for the integrated, real-time MCUs enabling them. To read about key motor drive design challenges for robotics, read our technical article, “<a href="https://e2e.ti.com/blogs_/b/industrial_strength/posts/how-motor-drive-innovations-are-helping-solve-robotic-movement-design-challenges" target="_blank">How motor drive innovations are helping solve robotic movement design challenges</a>.”<h2>A centralized architecture</h2>In a centralized system (<a href="https://www.ti.com/document-viewer/lit/html/SSZTD23#FIG_ZVY_HCV_G1C" target="_blank">Figure 1</a>), one MCU controls multiple axes. This approach is effective at handling heat dissipation in higher-power motor drivers (typically above 2-3 kW) that require large heat sinks and cooling fans. In this architecture, position data is usually acquired externally through a resolver board or aggregator connected to the encoders.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1717222997634-GUID-20240130-SS0I-NM5K-FRBN-8SQTBFPXBCCR-low.png" style="width: 100%px;" class="fr-fic fr-dib">Figure 1 Block diagram of a centralized motor-control architecture for multiple-axis systemsTypically, in this architecture, multiple power stages are on the same printed circuit board or in close proximity; thus, one MCU can control multiple axes. This approach simplifies the real-time control and synchronization between multiple axes, as there is no need for longer communication lines between multiple motor-control MCUs.The motor-control MCU/microprocessor in a centralized architecture mainly requires high-performing real-time processing cores such as Arm® Cortex®-R5F cores or digital signal processors, real time communication interfaces such as EtherCAT, and enough pulse-width modulation (PMW) channels and peripherals for voltage and current sensing. MCUs such as the AM2432 can enable scalable multiple-axis systems and offer real-time control peripherals for as many as six axes and real-time communications in a single chip.In the past, field-programmable gate arrays or application-specific integrated circuits were primarily used for centralized motor control in automated systems. However, the high level of integration and cost-effectiveness of Arm Cortex MCUs can help you meet your system’s performance requirements while also enabling design scalability and flexibility.While centralized control architectures can meet the performance and efficiency design requirements of high-powered automated systems such as heavy-payload industrial robots, they do require additional cables from the cabinet to the mechanical motor in the joint, as well as from the position sensors to the aggregators. Those wires do not only cost more, they also wear out and require maintenance.<h2>A decentralized architecture</h2>Decentralized architectures (<a href="https://www.ti.com/document-viewer/lit/html/SSZTD23#FIG_N21_HCV_G1C" target="_blank">Figure 2</a>) have recently grown in popularity for systems with lower power requirements, and have become the standard approach for collaborative robot (cobot) manipulators.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1717223024230-GUID-20240130-SS0I-W9T6-X4MK-G7DDQDDXB8LN-low.png" style="width: 100%px;" class="fr-fic fr-dib">Figure 2 Block diagram of a decentralized motor control architecture for single-axis systemsDecentralized architectures integrate multiple single-axis motor drives into each joint within the robot, connected and synchronized through a real-time communication interface such as EtherCAT. Each drive usually controls one single axis and handles certain safety functions locally. Therefore, each MCU needs real-time control and communication capabilities, motor-control peripherals for a single axis, three to six PWM channels, on-chip successive approximation register analog-to-digital converters, or delta-sigma modulator inputs.In these applications, the position sensor usually sits next to the MCU, so these MCUs need a digital or analog interface to read data from the position sensor. While this architecture requires more MCUs, cost reductions at the system level can be significant, since you’ll need less cabling between the power bus and communication interface. Modern real-time MCUs such as the TMS320F28P650DK integrate all necessary peripherals including safety peripherals, which can enable single- or two-chip solutions for an integrated axis in decentralized architectures with high performance at a small size.<h2>Conclusion</h2>While motors may not be the trendiest system in a robot – especially when compared to artificial intelligence-enabled systems – they are the “muscles” that keep factories moving. They might be the most crucial part of modern manufacturing, so selecting the right control device deserves a lot of consideration. As these devices become more integrated, additional capabilities such as edge computing and wireless connectivity may make their way into motor-control designs.

More integrated, higher-performance microcontrollers (MCUs) enable higher power efficiency and smoother and safer motion with much higher accuracy, therefore increasing productivity and automation.

How MCUs increase system performance in robot motor control designs

This article was written by Adam Taylor for Mouser Electronics<h2 id="isPasted">Introduction</h2>In this project, we will configure the AMD Kria™ KR260 Robotics Starter Kit to control a Trossen Robotics ReactorX 150 robot arm. This complex robot arm uses <a href="https://www.mouser.com/new/robotis/robotis-dynamixel-x-modules/" rel="nofollow" target="_blank" data-internal="false">ROBOTIS DYNAMIXEL</a> servos, which contain not only motors but also microcontrollers and networking capability. While this enables the complex motor drives needed for robotics applications, it also means that each servo has several different drive and control mechanisms. Typically, when working with servos and robotics applications of this complexity, significant processing is required to plan and address the robotic movement.To interact with and control such robots, engineers often use the <a href="https://www.ros.org/" rel="nofollow" target="_blank" data-internal="false">Robot Operating System (ROS)</a>, which runs on Linux. The most used version of ROS is ROS 2, which updated the ROS frameworks and tools to work with a wider variety of environments, provide support for real-time environments, and use significantly updated APIs. ROS 2 provides developers with functionalities such as hardware drivers, robot models, datatypes, and support for perception and simultaneous localization and mapping (SLAM). ROS 2 also provides a series of tools that aid in the development or operation of systems such as RViz, which provides a 3D visualization, and Gazebo, a simulator.ROS 2 is built around a graph architecture; within this architecture, processing takes place in nodes that can receive and post data about the node, such as sensor, control, planning, actuator positioning, or current state. Nodes are connected on the ROS graph by topics, which are communication pipelines to which nodes can publish data and receive information. Along with nodes and topics, a node may also advertise services. The services have a single result, such as capturing a frame of video, sampling a sensor, or opening an actuator.<h2>Bill of Materials</h2><ul><li><a href="https://www.mouser.com/new/xilinx/xilinx-kria-kr260-kit/" rel="nofollow" target="_blank" data-internal="false">AMD Kria™ KR260 Robotics Starter Kit</a></li><li>Monitor with DisplayPort™ compatibility</li><li>USB keyboard and mouse</li><li><a href="https://www.trossenrobotics.com/reactorx-150-robot-arm.aspx" rel="nofollow" target="_blank" data-internal="false">ROBOTIS ReactorX In150</a> robotic arm</li></ul><h2>Resources</h2><ul><li><a href="https://ubuntu.com/certified/202202-29985" rel="nofollow" target="_blank" data-internal="false">Ubuntu Linux distribution image for the AMD Kria™ KR260 Robotics Starter Kit</a> (Ubuntu)</li><li><a href="https://www.xilinx.com/products/som/kria/kr260-robotics-starter-kit/kr260-getting-started/setting-up-the-sd-card-image.html" rel="nofollow" target="_blank" data-internal="false">Getting Started with the AMD Kria KR260 Robotics Starter Kit</a> (AMD)</li><li><a href="https://sourceforge.net/projects/vcxsrv/files/vcxsrv/1.20.14.0/" rel="nofollow" target="_blank" data-internal="false">VcXsrv Windows X Server Files</a></li></ul><h2>Software Setup</h2>In this project, we will install the Trossen Robotics packages and ROS 2 on the AMD Kria™ KR260 Robotics Starter Kit, which will enable you to control the robot arm.<h3>Installing Ubuntu</h3>Start by downloading and installing the Ubuntu Linux distribution image for the AMD Kria™ KR260 kit, available in the Resources section. Once the image is available, flash the image to an SD card following the instructions provided by AMD in Getting Started with Kria KR260 Robotics Starter Kit in the Resources section.Once you have successfully booted the AMD Kria KR260 kit, you will need to update the Ubuntu installation to ensure we can install ROS 2 correctly.<ol><li>Install resolvconf.</li></ol><code>&nbsp; &nbsp;sudo apt update</code><code>&nbsp; &nbsp;sudo apt install resolvconf</code><ol start="2"><li>With resolvconf installed, ensure it is running.</li></ol><code>&nbsp; &nbsp;sudo systemctl status resolvconf.service</code><ol start="3"><li>Once you have confirmed the service is running, add your preferred DNS server. In this example, we use Googles DNS.</li></ol><code>&nbsp; &nbsp;echo "nameserver 8.8.8.8" | sudo tee -a /etc/resolvconf/resolv.conf.d/head</code><code>&nbsp; &nbsp;echo "nameserver 8.8.4.4" | sudo tee -a /etc/resolvconf/resolv.conf.d/head</code><ol start="4"><li>Because we made changes, we need to restart the services.</li></ol><code>&nbsp; &nbsp;sudo systemctl restart resolvconf.service</code><code>&nbsp; &nbsp;sudo systemctl restart systemd-resolved.service</code><ol start="5"><li>Prevent Ubuntu on the KR260 from going to sleep.</li></ol><code>&nbsp; &nbsp;sudo gsettings set org.gnome.desktop.session idle-delay 0</code><code>&nbsp; &nbsp;sudo systemctl mask suspend.target</code><h3>Installing ROS 2</h3>To install ROS 2 and the packages to control the Trossen Robotics X Series arm, we will modify the instructions slightly to those used for the Raspberry Pi, which, like the AMD Kria™ KR260 kit, uses the Arm64 (aarch64) instruction set architecture.The latest version of ROS 2 supported by Trossen’s libraries is ROS 2 Humble. Use the following commands to install ROS 2 and the Trossen libraries:<code>cd ~</code><code>sudo apt install curl</code><code>curl 'https://raw.githubusercontent.com/Interbotix/interbotix_ros_manipulators/main/interbotix_ros_xsarms/install/rpi4/xsarm_rpi4_install.sh' &gt; xsarm_rpi4_install.sh</code><code>sed -i 's/sudo apt-get update &amp;&amp; sudo apt -y upgrade/sudo apt-get update/g' xsarm_rpi4_install.sh</code><code>chmod +x xsarm_rpi4_install.sh</code><code>./xsarm_rpi4_install.sh -d humble -j rx150</code><h2>Demo Application</h2>With ROS 2 installed on the AMD Kria™ KR260 kit, we will run one of the provided demo applications. Open two terminal windows on the KR260.<ol><li>In the first terminal window, enter the following command:</li></ol><code>&nbsp; &nbsp;ros2 launch interbotix_xsarm_control xsarm_control.launch.py robot_model:=rx150</code><ol start="2"><li>In the second terminal, enter the following command:</li></ol><code>&nbsp; &nbsp;python3 /home/ubuntu/interbotix_ws/src/interbotix_ros_manipulators/interbotix_ros_xsarms/interbotix_xsarm_control/demos/python_ros2_api/bartender.py</code>This will run an application that demonstrates the control of the robot arm.<h3>Demo Breakdown</h3>By examining the code in this application, we can see how we can control the arm using the Python programming language. First, we need to import the InterbotixManipulatorXS arm package from the InterbotiX libraries we installed. To do this, enter the following command: <code>from interbotix_xs_modules.arm import InterbotixManipulatorXS</code>With these packages installed, we can start creating applications that manipulate the arm using a Python application.Looking at the example code, we can see the first thing we do is initialize it with the parameters of the robot, the model, type, and its end effector.With the robot (shown as <code>bot</code> in the code) defined, the application first checks the robot has the correct number of joints for the demo. Once it has been confirmed as being suitable for the demo, the robot is controlled by using several Python commands that set the pose, or single positions, as required. The gripper can also be opened and closed using Python calls.<code>bot = InterbotixManipulatorXS("rx150", "arm", "gripper")</code><code>&nbsp; &nbsp; if (bot.arm.group_info.num_joints &lt; 5):</code><code>&nbsp; &nbsp; &nbsp; &nbsp; print('This demo requires the robot to have at least 5 joints!')</code><code>&nbsp; &nbsp; &nbsp; &nbsp; sys.exit()</code><code>&nbsp; &nbsp; bot.arm.set_ee_pose_components(x=0.3, z=0.2)</code><code>&nbsp; &nbsp; bot.arm.set_single_joint_position("waist", np.pi/2.0)</code><code>&nbsp; &nbsp; bot.gripper.open()</code><code>&nbsp; &nbsp; bot.arm.set_ee_cartesian_trajectory(x=0.1, z=-0.16)</code><code>&nbsp; &nbsp; bot.gripper.close()</code>Finally, the demo ends by safely positioning the robot to its home position.<code>&nbsp; &nbsp; bot.arm.go_to_home_pose()</code><code>&nbsp; &nbsp; bot.arm.go_to_sleep_pose()</code>Knowing this, we can start creating own custom applications, as shown in <a href="https://youtu.be/g4xX4aowwI8" rel="nofollow" target="_blank" data-internal="false">this video</a>. But first, we need to be able to remotely develop applications on the AMD Kria™ KR260 board.<h2>Remote Development</h2>To develop our remote application, we will use Visual Studio Code on the development machine. The Python application we create using Visual Studio Code on our development machine will be in the file system of the AMD Kria™ KR260 kit. This also has the benefit of requiring no connected display, keyboard, or mouse, as we can access everything from the development environment.We also want to be able to remotely view the visualization of the robot arm as we develop our application (in simulation mode or actual execution).To enable remote development and visualization, we must establish SSH connection and enable X11 forwarding.<ol><li>Use Windows Powershell to install OpenSSH using the following commands.</li></ol><code>&nbsp; &nbsp;Get-WindowsCapability -Online | Where-Object Name -like 'OpenSSH*'</code><code>&nbsp; &nbsp;# Install the OpenSSH Client</code><code>&nbsp; &nbsp;Add-WindowsCapability -Online -Name OpenSSH.Client~~~~0.0.1.0</code><code>&nbsp; &nbsp;# Install the OpenSSH Server</code><code>&nbsp; &nbsp;Add-WindowsCapability -Online -Name OpenSSH.Server~~~~0.0.1.0</code><code>&nbsp; &nbsp;# Start the sshd service</code><code>&nbsp; &nbsp;Start-Service sshd</code><code>&nbsp; &nbsp;# OPTIONAL but recommended:/code&gt;</code><code>&nbsp; &nbsp;Set-Service -Name sshd -StartupType 'Automatic'</code><code>&nbsp; &nbsp;# Confirm the Firewall rule is configured. It should be created automatically by setup. Run the following to verify</code><code>&nbsp; &nbsp;if (!(Get-NetFirewallRule -Name "OpenSSH-Server-In-TCP" -ErrorAction SilentlyContinue | Select-Object Name, Enabled)) {</code><code>&nbsp; &nbsp; &nbsp; &nbsp; Write-Output "Firewall Rule 'OpenSSH-Server-In-TCP' does not exist, creating it..."</code><code>&nbsp; &nbsp; &nbsp; &nbsp; New-NetFirewallRule -Name 'OpenSSH-Server-In-TCP' -DisplayName 'OpenSSH Server (sshd)' -Enabled True -Direction Inbound -Protocol TCP -Action Allow -LocalPort 22</code><code>&nbsp; &nbsp;} else {</code><code>&nbsp; &nbsp; &nbsp; &nbsp; Write-Output "Firewall rule 'OpenSSH-Server-In-TCP' has been created and exists."</code><code>&nbsp; &nbsp;}</code><ol start="2"><li>With OpenSSH installed, create a key using the following command.</li></ol><code>&nbsp; &nbsp;ssh-keygen</code>By default, the system will save the keys to <code>C:\Users\&lt;user&gt;/.ssh/id_rsa.</code><ol start="3"><li>Exchange keys using the following command.</li></ol><code>&nbsp; &nbsp;type $env:USERPROFILE\.ssh\id_rsa.pub | ssh ubuntu@&lt;board_ip&gt; "cat &gt;&gt; .ssh/authorized_keys"</code>To establish a SSH connection from Visual Studio Code, we must first install the remote SSH option from the extensions (Figure 1).<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzE3MTQ3NTU5NTE3LUFNRHBfZmlnMS53ZWJwIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">Figure 1: SSH remote connection application. (Source: Mouser Electronics)Once this is installed, we can connect to the AMD Kria KR260 board (Figure 2), which will allow us to develop our applications remotely in the KR260 file system.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzE3MTQ3NTc0MDA4LUFNRHBfZmlnMi53ZWJwIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">Figure 2: Establishing an SSH remote connection. (Source: Mouser Electronics)To be able to use X11 forwarding, we must first install cXsrv Windows X Server (available in the Resources section) on our development machine. Run X Server and configure it. For screenshots fo this setup see <a href="https://resources.mouser.com/robotics/mastering-multi-axis-robotics-guide" target="_blank" rel="noopener noreferrer">here</a>. &nbsp;Connect to the display by running the following command in a command window on the development machine:<code>set DISPLAY=127.0.0.1:0.0</code>We can then initiate an SSH connection with the AMD Kria KR260 board and start developing our robot application.<code>ssh -Y &lt;user&gt;@&lt;board_ip&gt;</code>As this runs, the robot arm will move and you will see a visualization of this movement on X Server (Figure 7).<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzE3MTQ3Njc4MjM0LUFNRHBfZmlnNy53ZWJwIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">Figure 7: X Server robotic arm visualization (Source: Mouser Electronics)<h2 id="isPasted">Wrap Up</h2>Robotics can be complicated. In this project, we have seen that the AMD Kria™ KR260 kit can enable rapid development of robotic solutions using ROS 2. The KR260 provides the ability to accelerate features within the programmable logic element or to integrate further system control elements such as time-sensitive networking within the programmable logic.<hr>AMD, and the AMD Arrow logo, Kria, and combinations thereof are trademarks of Advanced Micro Devices, Inc. Other product names used herein are for identification purposes and may be trademarks of their respective owners.

In this project, we will configure the AMD Kria™ KR260 Robotics Starter Kit to control a Trossen Robotics ReactorX 150 robot arm. This complex robot arm uses ROBOTIS DYNAMIXEL servos, which contain not only motors but also microcontrollers and networking capability.

Mastering Multi-Axis Robotics: A Step-by-Step Guide

This article was discussed in our <a href="https://www.wevolver.com/profile/the.next.byte" rel="noopener noreferrer" target="_blank">Next Byte podcast</a>.&nbsp;<iframe height="200px" width="100%" frameborder="no" scrolling="no" seamless="" src="https://player.simplecast.com/eb21608e-dd64-4028-89db-ee2a5395b3ae?dark=false" class="fr-draggable" data-gtm-yt-inspected-23="true" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe> The full article will continue below.<hr>Earlier this year, a robot took the internet by storm by sautéing shrimp. In addition to this culinary feat, <a href="https://mobile-aloha.github.io/" data-extlink="" target="_blank">Mobile ALOHA</a> was shown performing other acts associated with the long-held dream of general-purpose household robots, such as vacuuming, doing laundry, and watering plants.While some of the tasks Mobile ALOHA has performed on video weren’t autonomous, its ability to someday perform nearly any daily chore is not far-fetched. Behind the dazzling demonstrations is a unique teleoperation structure that allows Mobile ALOHA to learn many complex activities, impressively fast.“We’re providing a general framework that allows people to show robots how to do a task and then allows robots to learn from what they were shown,” said <a href="https://profiles.stanford.edu/chelsea-finn" data-extlink="" target="_blank">Chelsea Finn</a>, assistant professor of computer science and of electrical engineering at Stanford. Finn advises the Mobile ALOHA team, which is led by computer science graduate students Zipeng Fu and Tony Z. Zhao. &nbsp;<iframe width="640" height="360" src="https://www.youtube.com/embed/P7FRO_6L-iA?&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true" data-gtm-yt-inspected-23="true" id="118103598" title="Stanford robot learns from humans">&nbsp;</iframe>Some general-use robots are programmed step-by-step and others, including Mobile ALOHA, learn through demonstration. But the Mobile ALOHA researchers took imitation learning a step further: The researchers strap themselves into a teleoperation system directly behind the robot’s arms and puppeteer the robot through the desired actions.Once Mobile ALOHA is operated through a task in a set environment about 50 times, powerful imitation learning algorithms help it make the leap to doing that task independently. (These algorithms are similar to the large language models behind popular chatbots, but for physical action instead of words.)So far, the researchers have taught Mobile ALOHA to autonomously put away a cooking pot in a cabinet, call an elevator, push in chairs, sauté shrimp, clean up a wine spill, and give high-fives. Future versions of the robot may be smaller with greater freedom of movement, and be easier for non-experts to operate.“The robot isn’t just a cool machine, we also wanted it to be fun and appeal to what people think a future robot should look like,” said Zhao.“Helping robots is a very promising future of the field where we – as AI researchers and roboticists – can make a positive impact in society,” said Fu.

What's the best way to train robots to perform everyday tasks? | Image courtesy of Kurt Hickman

Stanford engineers created a low-cost, mobile robot that quickly learned to sautée shrimp, put away dishes, and clean up spills. Other tasks aren't far behind.

Meet the robot that learned to saute shrimp

In this episode, we discuss how a team from Carnegie Mellon University spearheading non-invasive brain computer interface solutions has had a significant breakthrough in improving their accuracy.

Podcast: All The Brain Chip Implant Benefits & None of The Risks

This article was discussed in our <a href="https://www.wevolver.com/profile/the.next.byte" rel="noopener noreferrer" target="_blank">Next Byte podcast</a>.&nbsp;<iframe height="200px" width="100%" frameborder="no" scrolling="no" seamless="" src="https://player.simplecast.com/96f146e3-d84e-4bc9-8fa4-af61e7ce87ed?dark=false" class="fr-draggable" data-gtm-yt-inspected-9="true" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe>The full article will continue below.<hr>Pursuing a viable alternative to invasive brain-computer interfaces (BCIs) has been a continued research focus of Carnegie Mellon University’s <a href="https://www.cmu.edu/bme/helab/index.html" rel="noopener" target="_blank">He Lab</a>. In 2019, the group used a noninvasive BCI to successfully demonstrate, for the first time, that a mind-controlled robotic arm had the ability to continuously track and follow a computer cursor. As technology has improved, their AI-powered deep learning approach has become more robust and effective. In new work published in PNAS Nexus, the group demonstrates that humans can control continuous tracking of a moving object all by thinking about it, with unmatched performance.Noninvasive BCIs bring a host of advantages, in contrast to their invasive counterparts (think Neuralink or Synchron). These include increased safety, cost-effectiveness, and an ability to be used by numerous patients, as well as the general population. Amid these benefits, however, noninvasive BCIs face challenges because their recordings are less accurate and difficult to interpret.<iframe width="640" height="360" src="https://www.youtube.com/embed/EoKHw6bdoZs?&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true" data-gtm-yt-inspected-9="true" id="917213689" title="Bin He: Neuroengineering and Dynamic Brain Mapping">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</iframe>In He’s <a href="https://academic.oup.com/pnasnexus/article-lookup/doi/10.1093/pnasnexus/pgae145" rel="noopener" target="_blank" id="isPasted">recent study</a>, a group of 28 human participants were given a complex BCI task to track an object in a two-dimensional space all by thinking about it. During the task, an electroencephalography (EEG) method recorded their activity, from outside the brain. Using AI to train a deep neural network, the He group then directly decoded and interpreted human intentions for continuous object movement using the BCI sensor data. Overall, the work demonstrates the excellent performance of non-invasive BCI for a brain-controlled computerized device.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzE0NTE3ODg4OTAzLTA0MzAtZW0tbm9uaW52YXNpdmUtYmNpLnBuZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 100%px;" class="fr-fic fr-dib">Source:&nbsp;He Lab“The innovation in AI technology has enabled us to greatly improve the performance versus conventional techniques, and shed light for wide human application in the future,” expressed <a href="https://engineering.cmu.edu/directory/bios/he-bin.html" rel="noopener" target="_blank" id="isPasted">Bin He</a>, professor of <a href="https://www.cmu.edu/bme/" rel="noopener" target="_blank">biomedical engineering</a> at Carnegie Mellon University.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1714517922793-0430-em-2-noninvasive-bci.gif" style="width: 100%px;" class="fr-fic fr-dib">This video shows the cursor and target trajectories throughout a single 60-second trial with lag adjusted. The randomly moving target is represented by a yellow circle, while the cursor that the subject controlled using the BCI system is shown as a blue circle. A human subject controlled the blue cursor using the deep learning-based BCI decoder just by thinking about it. Source:&nbsp;He LabMoreover, the capability of the group’s AI-powered BCI suggests a direct application to continuously controlling a robotic device.“We are currently testing this AI-powered noninvasive BCI technology to control sophisticated tasks of a robotic arm,” said He. “Also, we are further testing its applicability to not only able-body subjects, but also stroke patients suffering motor impairments.” In a few years, this may lead to AI-powered assistive robots becoming available to a broad range of potential users.To this end, motor-impaired patients who are suffering from spinal cord injury, stroke, or other movement impairment, but do not want to receive an implant, stand to benefit immensely from research in this vein. “We keep pushing noninvasive neuroengineering solutions that can help everybody,” added He.This work was supported in part by the National Institute of Neurological Disorders and Stroke, National Center for Complementary and Integrative Health, and National Institute of Biomedical Imaging and Bioengineering.Other collaborators on the PNAS Nexus paper include the first authors Dylan Forenzo, BME Ph.D. student, and Hao Zhu, former BME Masters student; Jenn Shanahan, former lab technician; and Jaehyun Lim, BME undergraduate student.

Achieving a noteworthy milestone to advance noninvasive brain-controlled interfaces, researchers used AI technology to improve the decoding of human intention and control a continuously moving virtual object all by thinking about it, with unmatched performance.

Refined AI approach improves noninvasive BCI performance

In this episode, we talk about how a fashion company spun-off from MIT has developed the world’s first truly one-size-fits-all garment.

Podcast: Novel Fabrics + Robots -- Sustainable One-size-fits-all Fashion Revolution

A pick and place robot in an industrial setup

Understanding the inception, evolution, composition, and types of pick and place robots and their impact on various industrial sectors 

Pick and Place Robots: An In-Depth Guide to Their Functionality and Applications

This article was discussed in our <a href="https://www.wevolver.com/profile/the.next.byte" rel="noopener noreferrer" target="_blank">Next Byte podcast</a>.&nbsp;<iframe height="200px" width="100%" frameborder="no" scrolling="no" seamless="" src="https://player.simplecast.com/7a6ed573-f46a-40bb-b396-d38d1207aa2e?dark=false" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe> The full article will continue below.<hr>Until recently, bespoke tailoring — clothing made to a customer’s individual specifications — was the only way to have garments that provided the perfect fit for your physique. For most people, the cost of custom tailoring is prohibitive. But the invention of active fibers and innovative knitting processes is changing the textile industry.“We all wear clothes and shoes,” says Sasha MicKinlay MArch ’23, a recent graduate of the MIT Department of Architecture. “It’s a human need. But there’s also the human need to express oneself. I like the idea of customizing clothes in a sustainable way. This dress promises to be more sustainable than traditional fashion to both the consumer and the producer.”McKinlay is a textile designer and researcher at the Self-Assembly Lab who designed the <a href="https://selfassemblylab.mit.edu/4d-knit-dress" target="_blank">4D Knit Dress</a> with Ministry of Supply, a fashion company specializing in high-tech apparel. The dress combines several technologies to create personalized fit and style. Heat-activated yarns, computerized knitting, and robotic activation around each garment generates the sculpted fit. A team at Ministry of Supply led the decisions on the stable yarns, color, original size, and overall design.“Everyone’s body is different,” says Skylar Tibbits, associate professor in the Department of Architecture and founder of the Self-Assembly Lab. “Even if you wear the same size as another person, you're not actually the same.”<iframe width="640" height="360" src="https://player.vimeo.com/video/893959788" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true">&nbsp;&nbsp;&nbsp;&nbsp;</iframe>4D Knit Dress: Transforming Style. Video: Self-Assembly Lab<h2 id="isPasted">Active textiles</h2>Students in the Self-Assembly Lab have been working with dynamic textiles for several years. The yarns they create can change shape, change property, change insulation, or become breathable. Previous applications to tailor garments include <a href="https://selfassemblylab.mit.edu/active-textile-tailoring" target="_blank">making sweaters</a> and <a href="https://news.mit.edu/2022/form-fitting-face-mask-lavender-tessmer-0718" target="_blank">face masks</a>. Tibbits says the 4D Knit Dress is a culmination of everything the students have learned from working with active textiles.McKinlay helped produce the active yarns, created the concept design, developed the knitting technique, and programmed the lab’s industrial knitting machine. Once the garment design is programmed into the machine, it can quickly produce multiple dresses. Where the active yarns are placed in the design allows for the dress to take on a variety of styles such as pintucks, pleats, an empire waist, or a cinched waist.“The styling is important,” McKinlay says. “Most people focus on the size, but I think styling is what sets clothes apart. We’re all evolving as people, and I think our style evolves as well. After fit, people focus on personal expression.”<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEwMTM1NjY2MDgwLU1pbmlzdHJ5X0ZJTkFMX2RyZXNzZXMuanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">Designers explored this series of possible styles for the robotic heat activation of the 4D Knit Dress. Image courtesy of the Self-Assembly Lab.Danny Griffin MArch ’22, a current graduate student in architectural design, doesn’t have a background in garment making or the fashion industry. Tibbits asked Griffin to join the team due to his experience with robotics projects in construction. Griffin translated the heat activation process into a programmable robotic procedure that would precisely control its application.“When we apply heat, the fibers shorten, causing the textile to bunch up in a specific zone, effectively tightening the shape as if we’re tailoring the garment,” says Griffin. “There was a lot of trial and error to figure out how to orient the robot and the heat gun. The heat needs to be applied in precise locations to activate the fibers on each garment. Another challenge was setting the temperature and the timing for the heat to be applied.”It took a while to determine how the robot could&nbsp;reach all areas of the dress.“We couldn’t use a commercial heat gun — which is like a handheld hair dryer — because they’re too large,” says Griffin. “We needed a more compact design. Once we figured it out, it was a lot of fun to write the script for the robot to follow.”A dress can begin with one design — pintucks across the chest, for example — and be worn for months before having heat re-applied to alter its look. Subsequent applications of heat can tailor the dress further.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEwMTM1NjgyOTQyLVJvYm90aWNfYXJtX2F0X3dvcmsuanBlZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 100%px;" class="fr-fic fr-dib">Robotic heat activation can be seen during a demonstration at Ministry of Supply in Boston. Photo: Olivia Mintz<h2>Beyond fit and fashion</h2>Efficiently producing garments is a “big challenge” in the fashion industry, according to Gihan Amarasiriwardena ’11, the co-founder and president of Ministry of Supply.“A lot of times you'll be guessing what a season's style is,” he says. “Sometimes the style doesn't do well, or some sizes don’t sell out. They may get discounted very heavily or eventually they end up going to a landfill.”“Fast fashion” is a term that describes clothes that are inexpensive, trendy, and easily disposed of by the consumer. They are designed and produced quickly to keep pace with current trends. The 4D Knit Dress, says Tibbits, is the opposite of fast fashion. Unlike the traditional “cut-and-sew” process in the fashion industry, the 4D Knit Dress is made entirely in one piece, which virtually eliminates waste.“From a global standpoint, you don’t have tons of excess inventory because the dress is customized to your size,” says Tibbits.McKinlay says she hopes use of this new technology will reduce the amount of waste in inventory that retailers usually have at the end of each season.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEwMTM1NzEwMDIxLU1pbmlzdHJ5X0ZJTkFMX0RyYWZ0MDUgY29weS5qcGVnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">A complete 4D Knit Dress comes out of a Stoll industrial knitting machine after being produced in one piece, without any cutting or sewing. Photo courtesy of the Self-Assembly Lab.“The dress could be tailored in order to adapt to these changes in styles and tastes,” she says. “It may also be able to absorb some of the size variations that retailers need to stock. Instead of extra-small, small, medium, large, and extra-large sizes, retailers may be able to have one dress for the smaller sizes and one for the larger sizes. Of course, these are the same sustainability points that would benefit the consumer.”The Self-Assembly Lab has collaborated with Ministry of Supply on projects with active textiles for several years. Late last year, the team debuted the <a href="https://selfassemblylab.mit.edu/4d-knit-dress" target="_blank">4D Knit Dress</a> at the company’s flagship store in Boston, complete with a robotic arm working its way around a dress as customers watched. For Amarasiriwardena, it was an opportunity to gauge interest and receive feedback from customers interested in trying the dress on.“If the demand is there, this is something we can create quickly” unlike the usual design and manufacturing process, which can take years, says Amarasiriwardena.Griffin and McKinlay were on hand for the demonstration and pleased with the results. For Griffin, with the “technical barriers” overcome, he sees many different avenues for the project.“This experience leaves me wanting to try more,” he says.McKinlay too would love to work on more styles.“I&nbsp;hope this research project helps people rethink or reevaluate their relationship with clothes,” says McKinlay. “Right now when people purchase a piece of clothing it has only one ‘look.’ But, how exciting would it be to purchase one garment and reinvent it to change and evolve as you change or as the seasons or styles change? I'm hoping that's the takeaway that people will have.”

Caption:In late 2023 the MIT Self Assembly Lab and the high-tech fashion company Ministry of Supply debuted their 4D Knit Dress at the latter's store Boston, complete with a robotic arm working its way around a dress as customers watched. Credits:Image courtesy of MIT Self Assembly Lab

Developed by the Self-Assembly Lab, the 4D Knit Dress uses several technologies to create a custom design and a custom fit, while addressing sustainability concerns.

Is this the future of fashion?

In this episode, we delve into the cognitive strategies employed by researchers at EPFL to augment the human body with an additional robotic arm and learn about the profound impact of cognitive enhancements on the integration of advanced robotic limbs

Podcast: Why Robotic Limbs Are Critical To The Human Race

This article was discussed in our <a href="https://www.wevolver.com/profile/the.next.byte" rel="noopener noreferrer" target="_blank">Next Byte podcast</a>.&nbsp;<iframe height="200px" width="100%" frameborder="no" scrolling="no" seamless="" src="https://player.simplecast.com/85f51311-32bd-4a6e-953c-0d6777ee40fc?dark=false" class="fr-draggable" data-gtm-yt-inspected-9="true" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe> The full article will continue below.<hr>Neuroengineer Silvestro Micera develops advanced technological solutions to help people regain sensory and motor functions that have been lost due to traumatic events or neurological disorders. Until now, he had never before worked on enhancing the human body and cognition with the help of technology.<iframe width="640" height="360" src="https://www.youtube.com/embed/w78rRbaH7Wg?&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-gtm-yt-inspected-8="true" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true" data-gtm-yt-inspected-9="true" id="720627293" title="Cognitive strategies for augmenting the body with a wearable, robotic arm">&nbsp;</iframe>Now in a study published in Science Robotics, Micera and his team report on how diaphragm movement can be monitored for successful control of an extra arm, essentially augmenting a healthy individual with a third – robotic – arm.“This study opens up new and exciting opportunities, showing that extra arms can be extensively controlled and that simultaneous control with both natural arms is possible,” says Micera, Bertarelli Foundation Chair in Translational Neuroengineering at EPFL, and professor of Bioelectronics at Scuola Superiore Sant’Anna.The study is part of the <a href="https://nccr-robotics.ch/research/wearable-robotics/third-arm/" target="_blank" rel="noopener">Third-Arm project</a>, previously funded by the Swiss National Science Foundation (NCCR Robotics), that aims to provide a wearable robotic arm to assist in daily tasks or to help in search and rescue. Micera believes that exploring the cognitive limitations of third-arm control may actually provide gateways towards better understanding of the human brain.Micera continues, “The main motivation of this third arm control is to understand the nervous system. If you challenge the brain to do something that is completely new, you can learn if the brain has the capacity to do it and if it’s possible to facilitate this learning. We can then transfer this knowledge to develop, for example, assistive devices for people with disabilities, or rehabilitation protocols after stroke.”“We want to understand if our brains are hardwired to control what nature has given us, and we’ve shown that the human brain can adapt to coordinate new limbs in tandem with our biological ones,” explains Solaiman Shokur, co-PI of the study and EPFL Senior Scientist at the Neuro-X Institute. “It’s about acquiring new motor functions, enhancement beyond the existing functions of a given user, be it a healthy individual or a disabled one. From a nervous system perspective, it’s a continuum between rehabilitation and augmentation.”To explore the cognitive constraints of augmentation, the researchers first built a virtual environment to test a healthy user’s capacity to control a virtual arm using movement of his or her diaphragm. They found that diaphragm control does not interfere with actions like controlling one’s physiological arms, one’s speech or gaze.In this virtual reality setup, the user is equipped with a belt that measures diaphragm movement. Wearing a virtual reality headset, the user sees three arms: the right arm and hand, the left arm and hand, and a third arm between the two with a symmetric, six-fingered hand.“We made this hand symmetric to avoid any bias towards either the left or the right hand,” explains Giulia Dominijanni, PhD student at EPFL’s Neuro-X Institute.In the virtual environment, the user is then prompted to reach out with either the left hand, the right hand, or in the middle with the symmetric hand. In the real environment, the user holds onto an exoskeleton with both arms, which allows for control of the virtual left and right arms. Movement detected by the belt around the diaphragm is used for controlling the virtual middle, symmetric arm. The setup was tested on 61 healthy subjects in over 150 sessions.“Diaphragm control of the third arm is actually very intuitive, with participants learning to control the extra limb very quickly,” explains Dominijanni. “Moreover, our control strategy is inherently independent from the biological limbs and we show that diaphragm control does not impact a user’s ability to speak coherently.”The researchers also successfully tested diaphragm control with an actual robotic arm, a simplified one that consists of a rod that can be extended out, and back in. When the user contracts the diaphragm, the rod is extended out. In an experiment similar to the VR environment, the user is asked to reach and hover over target circles with her left or right hand, or with the robotic rod.Besides the diaphragm, but not reported in the study, vestigial ear muscles have also been tested for feasibility in performing new tasks. In this approach, a user is equipped with ear sensors and trained to use fine ear muscle movement to control the displacement of a computer mouse.“Users could potentially use these ear muscles to control an extra limb,” says Shokur, emphasizing that these alternative control strategies may help one day for the development of rehabilitation protocols for people with motor deficiencies.Part of the third arm project, previous studies regarding the <a href="https://actu.epfl.ch/news/a-smart-artificial-hand-for-amputees-merges-user-a/" target="_blank">control of robotic arms</a> have been focused on helping amputees. The latest Science Robotics study is a step beyond repairing the human body towards augmentation.“Our next step is to explore the use of more complex robotic devices using our various control strategies, to perform real-life tasks, both inside and outside of the laboratory. Only then will we be able to grasp the real potential of this approach,” concludes Micera.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzAyNTY4NDIyNzU0LTgxOTJ4NTQ2NC5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib">Martina Gini controls a simplified robotic arm with breathing. © 2023 EPFL / Alain Herzog, CC-BY-SA<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzAyNTY4NTQ4Mjg5LWV6Z2lmLTUtNDVjNDI4M2Y2Zi5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib">Silvestro Micera (left) and Leonardo Pollina (right) prepare for the third arm virtual reality experiment. © 2023 EPFL / Alain Herzog, CC-BY-SA

EPFL scientists show that breathing may be used to control a wearable extra robotic arm in healthy individuals, without hindering control of other parts of the body.

Cognitive strategies to augment the body with an extra robotic arm

Imagine that dinner could be prepared by a robot in your own home with just the push of a button. It sounds like something straight out of a far-flung future. Now, researchers at CMU are laying the groundwork for this technology with artificial intelligence.&nbsp;Alison Bartch, Atharva Dikshit, and Abraham George, under the guidance of <a href="https://engineering.cmu.edu/directory/bios/barati-farimani-amir.html" target="_blank">Amir Barati Farimani</a>, assistant professor of <a href="https://www.meche.engineering.cmu.edu/index.html" rel="noopener" target="_blank">mechanical engineeringOpens in new window</a>, trained a robot arm on different fruit slices and shapes to help improve its accuracy with cutting and moving real pieces of food on a cutting board.&nbsp;“So I’m cooking and have a cluttered cutting board. I’m reasoning about how to chop and how to select the objects that I want to cut, without cutting things that I don’t want,” said Bartch, a Ph.D. candidate in mechanical engineering. “It’s something that humans are pretty good at. And it’s a surprisingly difficult problem for robots.”<blockquote>It’s something that humans are pretty good at. And it’s a surprisingly difficult problem for robots.<cite>Alison Bartch<cite id="isPasted">,&nbsp;Ph.D. student<cite id="isPasted">, Mechanical Engineering</cite></cite> </cite></blockquote>The researchers combined two vision foundational models—models trained on large visual data sets—to help the robot arm recognize the shape and the type of fruit and vegetable slices. The AI would then determine what motion to use on these slices to produce the correct results with 70% accuracy.One of the models was only trained on whole fruits and vegetables, so the researchers needed to fine-tune the AI to more accurately recognize slices with a relatively small amount of newly collected data. According to Bartch, their fine-tuned framework can potentially be used for other related research; the combined foundational models, however, can be used for a variety of other AI projects in the future.“That’s just one of the things in a lot of robotics and machine learning—a lot of the data sets are already collected,” said George, also a Ph.D. candidate in mechanical engineering.&nbsp;Bartch said that the research focuses more on the “practicalities” of cutting food, such as producing the correct number of slices and moving pieces out of the way, making this research more applicable to a real kitchen than other research.“For this work, we focused more on the pipeline surrounding it, as opposed to the actual chop action,” Bartch said. “You want to pick which object to cut. You want to clear the scene before you cut. After you cut, detecting where the objects are then and continuing to plan. A lot of existing work focused on cutting assumed the position of the vegetable and that there is nothing else in the environment.”&nbsp;However, it will still be a while before AI will be chopping vegetables in homes. According to Bartch, there is still a lot of research and fine-tuning needed, and their robot still needs some human input. It’s difficult to replicate human reasoning in machines, even with tasks many humans find simple.“I think there is definitely a really long way to go before it’s in a person’s house.” Bartch said. “Because reasoning about cooking is really difficult. When you want to go into more practical situations, you need the system to be able to reason when the food is done.Atharva Dikshit, a master’s student in mechanical engineering, also contributed to this research. This research was submitted to the Journal of Intelligent and Robotic Systems.

Researchers at CMU combined two vision foundational models—models trained on large visual data sets—to help a robot arm recognize the shape and the type of fruit and vegetable slices.

Chop, chop: Improving food prep with the power of AI

Engineers at Princeton University and Google have come up with a new way to teach robots to know when they don’t know. The technique involves quantifying the fuzziness of human language and using that measurement to tell robots when to ask for further directions. Telling a robot to pick up a bowl from a table with only one bowl is fairly clear. But telling a robot to pick up a bowl when there are five bowls on the table generates a much higher degree of uncertainty — and triggers the robot to ask for clarification.Because tasks are typically more complex than a simple “pick up a bowl” command, the engineers use large language models (LLMs) — the technology behind tools such as ChatGPT — to gauge uncertainty in complex environments. LLMs are bringing robots powerful capabilities to follow human language, but LLM outputs are still frequently unreliable, said <a href="https://engineering.princeton.edu/faculty/anirudha-majumdar" target="_blank">Anirudha Majumdar</a>, an assistant professor of <a href="https://mae.princeton.edu/" target="_blank">mechanical and aerospace engineering</a> at Princeton and the senior author of a <a href="https://openreview.net/forum?id=4ZK8ODNyFXx" target="_blank">study</a> outlining the new method.“Blindly following plans generated by an LLM could cause robots to act in an unsafe or untrustworthy manner, and so we need our LLM-based robots to know when they don’t know,” said Majumdar.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1701898003603-knowno-ambiguities_WEB-2048x871.jpg" style="width: 766px;" class="fr-fic fr-dib">The researchers tested their method on a simulated robotic arm and on two types of robot hardware. The most complex experiments (left) involved a robotic arm mounted on a wheeled platform and placed in an office kitchen with a microwave and a set of recycling, compost and trash bins. Another set of experiments used a tabletop robotic arm (right) tasked with sorting a set of toy food items into two different categories; a setup with a left and right arm (center) added an additional layer of ambiguity. Images by the researchersThe system also allows a robot’s user to set a target degree of success, which is tied to a particular uncertainty threshold that will lead a robot to ask for help. For example, a user would set a surgical robot to have a much lower error tolerance than a robot that’s cleaning up a living room.“We want the robot to ask for enough help such that we reach the level of success that the user wants. But meanwhile, we want to minimize the overall amount of help that the robot needs,” said&nbsp;<a href="https://allenzren.github.io/" target="_blank">Allen Ren</a>, a graduate student in mechanical and aerospace engineering at Princeton and the study’s lead author. Ren received a&nbsp;<a href="https://www.corl2023.org/awards" target="_blank">best student paper award</a> for his Nov. 8 presentation at the Conference on Robot Learning in Atlanta. The new method produces high accuracy while reducing the amount of help required by a robot compared to other methods of tackling this issue.The researchers tested their method on a simulated robotic arm and on two types of robots at Google facilities in New York City and Mountain View, California, where Ren was working as a student research intern. One set of hardware experiments used a tabletop robotic arm tasked with sorting a set of toy food items into two different categories; a setup with a left and right arm added an additional layer of ambiguity.The most complex experiments involved a robotic arm mounted on a wheeled platform and placed in an office kitchen with a microwave and a set of recycling, compost and trash bins. In one example, a human asks the robot to “place the bowl in the microwave,” but there are two bowls on the counter — a metal one and a plastic one.The robot’s LLM-based planner generates four possible actions to carry out based on this instruction, like multiple-choice answers, and each option is assigned a probability. Using a statistical approach called conformal prediction and a user-specified guaranteed success rate, the researchers designed their algorithm to trigger a request for human help when the options meet a certain probability threshold. In this case, the top two options — place the plastic bowl in the microwave or place the metal bowl in the microwave — meet this threshold, and the robot asks the human which bowl to place in the microwave.In another example, a person tells the robot, “There is an apple and a dirty sponge … It is rotten. Can you dispose of it?” This does not trigger a question from the robot, since the action “put the apple in the compost” has a sufficiently higher probability of being correct than any other option.“Using the technique of conformal prediction, which quantifies the language model’s uncertainty in a more rigorous way than prior methods, allows us to get to a higher level of success” while minimizing the frequency of triggering help, said the study’s senior author&nbsp;<a href="https://engineering.princeton.edu/faculty/anirudha-majumdar" target="_blank">Anirudha Majumdar</a>, an assistant professor of mechanical and aerospace engineering at Princeton.Robots’ physical limitations often give designers insights not readily available from abstract systems. Large language models “might talk their way out of a conversation, but they can’t skip gravity,” said coauthor&nbsp;<a href="https://andyzeng.github.io/" target="_blank">Andy Zeng</a>, a research scientist at Google DeepMind. “I’m always keen on seeing what we can do on robots first, because it often sheds light on the core challenges behind building generally intelligent machines.”Ren and Majumdar began collaborating with Zeng after he gave a talk as part of the&nbsp;<a href="https://robo.princeton.edu/seminar" target="_blank">Princeton Robotics Seminar</a> series, said Majumdar. Zeng, who earned a computer science Ph.D. from Princeton in 2019, outlined Google’s efforts in using LLMs for robotics, and brought up some open challenges. Ren’s enthusiasm for the problem of calibrating the level of help a robot should ask for led to his internship and the creation of the new method.“We enjoyed being able to leverage the scale that Google has” in terms of access to large language models and different hardware platforms, said Majumdar.Ren is now extending this work to problems of active perception for robots: For instance, a robot may need to use predictions to determine the location of a television, table or chair within a house, when the robot itself is in a different part of the house. This requires a planner based on a model that combines vision and language information, bringing up a new set of challenges in estimating uncertainty and determining when to trigger help, said Ren.The paper, “<a href="https://openreview.net/forum?id=4ZK8ODNyFXx" target="_blank">Robots That Ask for Help: Uncertainty Alignment for Large Language Model Planners</a>,” was presented Nov. 8 at the Conference on Robot Learning. In addition to Ren, Zeng and Majumdar, coauthors include Anushri Dixit and Alexandra Bodrova of Princeton; and Sumeet Singh, Stephen Tu, Noah Brown, Peng Xu, Leila Takayama, Fei Xia, Jake Varley, Zhenjia Xu and Dorsa Sadigh of Google DeepMind. The research was supported in part by the U.S. National Science Foundation and the Office of Naval Research.

Engineers at Princeton University and Google have come up with a new way to teach robots to know when they don’t know and ask for clarification from a human. Photo by the researchers

Modern robots know how to sense their environment and respond to language, but what they don’t know is often more important than what they do know. Teaching robots to ask for help is key to making them safer and more efficient.

How do you make a robot smarter? Program it to know what it doesn't know.

Imagine you’re visiting a friend abroad, and you look inside their fridge to see what would make for a great breakfast. Many of the items initially appear foreign to you, with each one encased in unfamiliar packaging and containers. Despite these visual distinctions, you begin to understand what each one is used for and pick them up as needed.Inspired by humans' ability to handle unfamiliar objects, a group from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) designed Feature Fields for Robotic Manipulation (<a href="https://arxiv.org/abs/2308.07931" target="_blank">F3RM</a>), a system that blends 2D images with foundation model features into 3D scenes to help robots identify and grasp nearby items. F3RM can interpret open-ended language prompts from humans, making the method helpful in real-world environments that contain thousands of objects, like warehouses and households.F3RM offers robots the ability to interpret open-ended text prompts using natural language, helping the machines manipulate objects. As a result, the machines can understand less-specific requests from humans and still complete the desired task. For example, if a user asks the robot to “pick up a tall mug,” the robot can locate and grab the item that best fits that description.“Making robots that can actually generalize in the real world is incredibly hard,” says Ge Yang, postdoc at the National Science Foundation AI Institute for Artificial Intelligence and Fundamental Interactions and MIT CSAIL. “We really want to figure out how to do that, so with this project, we try to push for an aggressive level of generalization, from just three or four objects to anything we find in MIT’s Stata Center. We wanted to learn how to make robots as flexible as ourselves, since we can grasp and place objects even though we've never seen them before.”<h2>Learning “what’s where by looking”</h2>The method could assist robots with picking items in large fulfillment centers with inevitable clutter and unpredictability. In these warehouses, robots are often given a description of the inventory that they're required to identify. The robots must match the text provided to an object, regardless of variations in packaging, so that customers’ orders are shipped correctly. For example, the fulfillment centers of major online retailers can contain millions of items, many of which a robot will have never encountered before. To operate at such a scale, robots need to understand the geometry and semantics of different items, with some being in tight spaces. With F3RM’s advanced spatial and semantic perception abilities, a robot could become more effective at locating an object, placing it in a bin, and then sending it along for packaging. Ultimately, this would help factory workers ship customers’ orders more efficiently.“One thing that often surprises people with F3RM is that the same system also works on a room and building scale, and can be used to build simulation environments for robot learning and large maps,” says Yang. “But before we scale up this work further, we want to first make this system work really fast. This way, we can use this type of representation for more dynamic robotic control tasks, hopefully in real-time, so that robots that handle more dynamic tasks can use it for perception.”The MIT team notes that F3RM’s ability to understand different scenes could make it useful in urban and household environments. For example, the approach could help personalized robots identify and pick up specific items. The system aids robots in grasping their surroundings — both physically and perceptively.“Visual perception was defined by David Marr as the problem of knowing ‘what is where by looking,’” says senior author Phillip Isola, MIT associate professor of electrical engineering and computer science and CSAIL principal investigator. “Recent foundation models have gotten really good at knowing what they are looking at; they can recognize thousands of object categories and provide detailed text descriptions of images. At the same time, radiance fields have gotten really good at representing where stuff is in a scene. The combination of these two approaches can create a representation of what is where in 3D, and what our work shows is that this combination is especially useful for robotic tasks, which require manipulating objects in 3D.”<h2>Creating a “digital twin”</h2>F3RM begins to understand its surroundings by taking pictures on a selfie stick. The mounted camera snaps 50 images at different poses, enabling it to build a <a href="https://www.matthewtancik.com/nerf" target="_blank">neural radiance field</a> (NeRF), a deep learning method that takes 2D images to construct a 3D scene. This collage of RGB photos creates a “digital twin” of its surroundings in the form of a 360-degree representation of what’s nearby.In addition to a highly detailed neural radiance field, F3RM also builds a feature field to augment geometry with semantic information. The system uses&nbsp;<a href="https://openai.com/research/clip" target="_blank">CLIP</a>, a vision foundation model trained on hundreds of millions of images to efficiently learn visual concepts. By reconstructing the 2D CLIP features for the images taken by the selfie stick, F3RM effectively lifts the 2D features into a 3D representation.<h2>Keeping things open-ended</h2>After receiving a few demonstrations, the robot applies what it knows about geometry and semantics to grasp objects it has never encountered before. Once a user submits a text query, the robot searches through the space of possible grasps to identify those most likely to succeed in picking up the object requested by the user. Each potential option is scored based on its relevance to the prompt, similarity to the demonstrations the robot has been trained on, and if it causes any collisions. The highest-scored grasp is then chosen and executed. To demonstrate the system’s ability to interpret open-ended requests from humans, the researchers prompted the robot to pick up Baymax, a character from Disney’s “Big Hero 6.” While F3RM had never been directly trained to pick up a toy of the cartoon superhero, the robot used its spatial awareness and vision-language features from the foundation models to decide which object to grasp and how to pick it up. F3RM also enables users to specify which object they want the robot to handle at different levels of linguistic detail. For example, if there is a metal mug and a glass mug, the user can ask the robot for the “glass mug.” If the bot sees two glass mugs and one of them is filled with coffee and the other with juice, the user can ask for the “glass mug with coffee.” The foundation model features embedded within the feature field enable this level of open-ended understanding.“If I showed a person how to pick up a mug by the lip, they could easily transfer that knowledge to pick up objects with similar geometries such as bowls, measuring beakers, or even rolls of tape. For robots, achieving this level of adaptability has been quite challenging,” says MIT PhD student, CSAIL affiliate, and co-lead author William Shen. “F3RM combines geometric understanding with semantics from foundation models trained on internet-scale data to enable this level of aggressive generalization from just a small number of demonstrations.” Shen and Yang wrote the paper under the supervision of Isola, with MIT professor and CSAIL principal investigator Leslie Pack Kaelbling and undergraduate students Alan Yu and Jansen Wong as co-authors. The team was supported, in part, by Amazon.com Services, the National Science Foundation, the Air Force Office of Scientific Research, the Office of Naval Research’s Multidisciplinary University Initiative, the Army Research Office, the MIT-IBM Watson AI Lab, and the MIT Quest for Intelligence. Their work will be presented at the 2023 Conference on Robot Learning.

Feature Fields for Robotic Manipulation (F3RM) enables robots to interpret open-ended text prompts using natural language, helping the machines manipulate unfamiliar objects. The system’s 3D feature fields could be helpful in environments that contain thousands of objects, such as warehouses. Images courtesy of the researchers.

By blending 2D images with foundation models to build 3D feature fields, a new MIT method helps robots understand and manipulate nearby objects with open-ended language prompts.

Using language to give robots a better grasp of an open-ended world

&nbsp; Achieving precision is&nbsp;a must&nbsp;in ultrasonic inspections, but&nbsp;it's&nbsp;not always straightforward due to&nbsp;some&nbsp;challenges like interference and noise. These issues can arise from various sources, including surface rust and environmental dust. To address these challenges,&nbsp;there's&nbsp;an exciting development on the horizon: the introduction of ultrasonic testing (UT) UAVs, often referred to as UT drones or UT flying robots. These innovative robots come equipped with&nbsp;state-of-the-art&nbsp;technology and, importantly, enhance safety by&nbsp;eliminating&nbsp;the need for human inspectors to enter high-risk areas.&nbsp;What's more, they have the capability to reach elevated inspection sites, effectively reducing the risks associated with working at heights while ensuring highly accurate results.&nbsp; &nbsp; &nbsp;One of the standout players in this field is SKYRON,&nbsp;the&nbsp;intelligent tethered&nbsp;flying&nbsp;robot equipped with a customized 38DL PLUS ultrasonic thickness gauge from Olympus. This technology not only enhances the safety of inspections but also elevates precision. The 38DL PLUS gauge is designed to tackle challenging conditions, whether that involves a rusty surface, a dusty environment,&nbsp;high temperatures, or access to difficult spaces. SKYRON can conduct meticulous inspections even in challenging locations.&nbsp;&nbsp; &nbsp; <img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1699488071311-1699488071311.png" class="fr-fic fr-dii">&nbsp;Moreover, what sets SKYRON apart is its ingenious built-in brush system, integrated into its rotary arm. This system is engineered to smooth rough surfaces and remove debris before an inspection, ensuring clean and&nbsp;accurate&nbsp;readings.&nbsp;It's&nbsp;a critical feature that ensures the reliability of the collected data. Furthermore,&nbsp;there's&nbsp;no need to interrupt the inspection process to change the brush head since&nbsp;it's&nbsp;seamlessly integrated into SKYRON's rotary arm. Additionally, before each inspection, SKYRON&nbsp;applies&nbsp;couplant&nbsp;gel, further streamlining the process.&nbsp;SKYRON brings even more improvements to the table. It boasts a continuous power supply, allowing it to fly for an unlimited duration, which is particularly&nbsp;advantageous&nbsp;for projects&nbsp;where&nbsp;relying on batteries would be inconvenient. The constant power supply also enables a higher payload capacity,&nbsp;facilitating&nbsp;the integration of various sensors, including advanced NDT,&nbsp;DFT,&nbsp;PAUT,&nbsp;and an array of robotic features.&nbsp;SKYRON is not just a UAV;&nbsp;it's&nbsp;a versatile platform that can seamlessly incorporate various sensors and features. It ensures safe and secure operations, minimizing the risk of unexpected issues during flights. Moreover, it guarantees&nbsp;accurate&nbsp;contact-based inspections of critical structures. SKYRON&nbsp;can fly,&nbsp;attach&nbsp;and measure multiple assets, capturing precise thickness measurements and conducting corrosion checks. What truly sets it apart is its adaptability, making it highly effective in challenging environments, and demanding conditions where&nbsp;maintaining&nbsp;a clear line of sight can be a challenge. In summary, SKYRON stands as a valuable&nbsp;tool&nbsp;for addressing a variety of inspection challenges and spearheading a new era of ultrasonic precision.&nbsp;In conclusion, SKYRON, equipped with its remarkable 38DL PLUS ultrasonic thickness gauge and ingenious built-in brush system,&nbsp;embodies&nbsp;the future of ultrasonic inspections. The seamless integration of these advanced features not only enhances the safety of inspections but also ensures the highest level of precision, regardless of the conditions. SKYRON&nbsp;doesn't&nbsp;just&nbsp;prepares&nbsp;a new era of ultrasonic precision; it serves as a shining example of what's possible when technology and innovation join forces. With its adaptability and versatility,&nbsp;it&nbsp;is ready&nbsp;to tackle a wide array of inspection challenges and&nbsp;help to&nbsp;redefine the standards of excellence in the field. As the skies&nbsp;open up&nbsp;to these remarkable flying robots.&nbsp;&nbsp;

Achieving precision is a must in ultrasonic inspections, but it's not always straightforward

Advancing Ultrasonic Inspections

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Unitree Robotics

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Canonical

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DEEP Robotics

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Robotic Arms

Robotic arms in the industry based on PID loop 

This guide delves into the basics and components of PID control, implementation, application examples, impacts, challenges, and solutions aiming to provide readers with a thorough understanding of this integral concept.

PID Controller & Loops: A Comprehensive Guide to Understanding and Implementation

Articulated robots&nbsp;are highly versatile&nbsp;industrial robots&nbsp;that have been used in various industries to perform a wide range of tasks. These robots are designed with a series of interconnected segments, known as links, which are attached through movable joints. This design allows them to move with a high degree of flexibility and dexterity, making them ideal for performing complex tasks requiring high precision.In this article, we will delve deeper into the specifics of&nbsp;articulated robots, explore the various&nbsp;control systems&nbsp;that they use, discuss their advantages and limitations, and examine their applications across different industries. We will also look at the challenges of operating&nbsp;articulated robots&nbsp;and discuss future trends and developments in this field.<h1 dir="ltr">Introduction</h1>The history of&nbsp;articulated robots&nbsp;dates back to the 1950s, when they were first introduced for use in&nbsp;manufacturing processes. Since then, they have evolved significantly and become integral to many industries, including&nbsp;automotive, aerospace, healthcare, and research.The importance of&nbsp;articulated robots&nbsp;lies in their ability to improve efficiency, accuracy, and safety in various operations. These robots are designed to perform repetitive and dangerous tasks that are often too difficult or hazardous for humans to undertake. As a result, they have helped to increase productivity and reduce the risk of workplace accidents.Suggested Reading:<a href="https://www.wevolver.com/article/7-types-of-industrial-robots-advantages-disadvantages-applications-and-more" target="_blank">&nbsp;7 Types of Industrial Robots: Advantages, Disadvantages, Applications, and More</a><h1 dir="ltr">Anatomy of Articulated Robots</h1>Articulated robots&nbsp;comprise several interconnected segments called links, which are connected through joints. These joints are designed to allow the robot to move with a high degree of flexibility, precision, and dexterity. The links of&nbsp;articulated robots&nbsp;can range from two to six or more, with each link providing a degree of freedom (DOF) that enables the robot to move in various directions.The joints used in&nbsp;articulated robots&nbsp;can be classified into two types: revolute and prismatic. Revolute joints are&nbsp;rotary joints&nbsp;that allow the robot to rotate along an axis. Prismatic joints, on the other hand, are linear joints that enable the robot to move in a straight line.Articulated robots&nbsp;are designed to have multiple&nbsp;degrees of freedom&nbsp;(DOF), which is the number of independent parameters that determine the robot's position and orientation. This allows the robot to move in a wide range of directions and positions, making it highly versatile. The DOF of&nbsp;articulated robots&nbsp;can range from two to six or more, with each DOF representing a degree of movement or rotation.The most common DOF configurations for&nbsp;articulated robots&nbsp;are 4-DOF and 6-DOF. 4-DOF robots typically have three revolute joints and one prismatic joint, while 6-DOF robots have three revolute joints and three prismatic joints. The additional&nbsp;degrees of freedom&nbsp;in 6-DOF robots allow for greater flexibility and precision in their movements.In addition to the links and joints,&nbsp;articulated robots&nbsp;also have&nbsp;end-effectors, which are the devices attached to the&nbsp;articulated&nbsp;robot arm&nbsp;that allow it to perform specific tasks.&nbsp;End-effectors&nbsp;can include grippers, suction cups, and other specialized tools that are designed to manipulate objects with precision and accuracy.Suggested Reading:<a href="https://www.wevolver.com/article/end-effector" target="_blank" rel="noopener noreferrer">&nbsp;What are End Effectors in Robotics? Types of End Effectors, Applications, Future</a><h1 dir="ltr">Control Systems of Articulated Robots</h1>The&nbsp;control systems&nbsp;of&nbsp;articulated robots&nbsp;are crucial in determining the robot's movements, actions, and overall performance. The&nbsp;robot control systems&nbsp;are responsible for monitoring the robot's position, speed, and orientation and making adjustments as needed to ensure that the robot moves accurately and precisely.There are two main types of&nbsp;control systems&nbsp;used in&nbsp;articulated robots: open-loop and closed-loop&nbsp;control systems. Open-loop&nbsp;control systems&nbsp;are used in simple, repetitive tasks where the robot's movement can be pre-programmed and does not require real-time feedback. Closed-loop&nbsp;control systems, on the other hand, use sensors to monitor the robot's movements and make adjustments in real-time to ensure precise and accurate movements.Sensors are an essential component of the&nbsp;control systems&nbsp;in&nbsp;articulated robots. They are used to monitor the robot's movements, track its position and orientation, and detect any errors or deviations from the intended path. Common sensors used in&nbsp;articulated robots&nbsp;include encoders, accelerometers, and proximity sensors.<iframe width="640" height="360" src="https://www.youtube.com/embed/HgDEqlhjhrE?&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true" data-gtm-yt-inspected-11="true" id="533422796" title="Articulated Robot - Servo Motor Production Line | Delta Industrial Automation" data-gtm-yt-inspected-10="true" data-gtm-yt-inspected-9="true" data-gtm-yt-inspected-23="true">&nbsp;</iframe>A video showcasing&nbsp;articulated robot&nbsp;application&nbsp;on a&nbsp;production line. Notice how the robot displays a wide&nbsp;range of motions&nbsp;in the&nbsp;workspace.<a href="https://www.wevolver.com/article/what-is-an-actuator-principles-classification-and-applications" target="_blank" rel="noopener noreferrer">Actuators are another critical component of the control systems</a> in articulated robots. Actuators are responsible for moving the robot's joints and links, enabling it to move with precision and accuracy. The most commonly used actuators in articulated robots are electric motors (such as the servo motor), hydraulic cylinders, and pneumatic cylinders.The&nbsp;control systems&nbsp;of&nbsp;articulated robots&nbsp;are typically operated through a computer interface. The operator can program the robot's movements, set its parameters, and monitor its performance through a graphical user interface (GUI). The GUI provides real-time feedback on the robot's movements, allowing the operator to make adjustments as needed to ensure that the robot moves accurately and precisely.In recent years, there have been significant advancements in the&nbsp;control systems&nbsp;of&nbsp;articulated robots, with the integration of artificial intelligence (AI) and machine learning (ML) technologies. These technologies enable the robot to learn and adapt to its environment, improving its performance and increasing its flexibility in performing tasks.<h1 dir="ltr">Advantages and Limitations of Articulated Robots</h1>Articulated robots offer several advantages over traditional industrial automation methods. Some of these advantages include:<ol><li dir="ltr">Flexibility: Articulated robots have a high degree of flexibility and dexterity, enabling them to perform a wide range of tasks in various industries.</li><li dir="ltr">Precision: The use of advanced control systems, sensors, and actuators allows articulated robots to perform tasks with a high degree of precision and accuracy.</li><li dir="ltr">Efficiency:&nbsp;Articulated robots&nbsp;can perform tasks with greater speed and efficiency than human workers, resulting in increased productivity and reduced labor costs.</li><li dir="ltr">Safety:&nbsp;Articulated robots&nbsp;can perform tasks that are hazardous or dangerous for human workers, reducing the risk of workplace accidents and injuries.</li></ol>However, there are also limitations to the use of&nbsp;articulated robots. Some of these limitations include the following:<ol><li dir="ltr">Cost:&nbsp;Articulated robots&nbsp;can be expensive to purchase and maintain, making them less accessible to small and medium-sized businesses.</li><li dir="ltr">Complex Programming:&nbsp;Programming&nbsp;articulated robots&nbsp;can be complex and time-consuming, requiring specialized knowledge and expertise.</li><li dir="ltr">Maintenance:&nbsp;The maintenance of&nbsp;articulated robots&nbsp;can be challenging, requiring specialized skills and equipment.</li><li dir="ltr">Space requirements:&nbsp;Articulated robots&nbsp;require a significant amount of space to operate, which can be a limitation in smaller facilities.</li><li dir="ltr">Lack of adaptability:&nbsp;Articulated robots&nbsp;may struggle to perform tasks in constantly changing or unpredictable environments, as they are programmed to perform specific tasks in a set environment.</li></ol>While articulated robots offer several advantages over traditional industrial automation methods, it is important to consider their limitations and potential challenges before implementing them in a business or manufacturing setting.<h1 dir="ltr">Applications of Articulated Robots</h1>Articulated robots&nbsp;are used in a wide range of industries and applications, including manufacturing, healthcare, and entertainment. Here are some of the most common applications of&nbsp;articulated robots:<ol><li dir="ltr">Manufacturing:&nbsp;Articulated robots&nbsp;are widely used in manufacturing industries for tasks such as assembly, welding, painting, packaging, and&nbsp;material handling. These robots can perform these tasks with high precision and speed, resulting in increased productivity and efficiency.</li><li dir="ltr">Healthcare:&nbsp;Articulated robots&nbsp;are increasingly being used in healthcare applications, such as surgery and rehabilitation. They can perform surgical procedures with greater precision and accuracy than human surgeons, reducing recovery times and improving patient outcomes.</li><li dir="ltr">Entertainment:&nbsp;Articulated robots&nbsp;are used in the entertainment industry for tasks such as animatronics, special effects, and puppetry. They can be programmed to perform complex movements and actions, resulting in lifelike performances and realistic special effects.</li></ol><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjc5ODM3Njc5MTg1LTE2Nzk4Mzc2NzkxODUucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" width="720" height="480" id="isPasted" class="fr-fic fr-dii" alt="articulated-robot-arm-manufacturing-application">Fig. 1: Articulated robot used for pick and place application<ol start="4"><li dir="ltr">Agriculture:&nbsp;Articulated robots&nbsp;are used in agriculture for tasks such as harvesting, planting, and spraying. By operating in difficult and dangerous environments,&nbsp;articulated robots&nbsp;reduce the risk of injury to human workers and improve the efficiency of the agricultural process.</li><li dir="ltr">Military and Defense:&nbsp;Articulated robots&nbsp;are used in military and defense applications for tasks such as bomb disposal and reconnaissance. These robots can operate in hazardous environments, reducing the risk of injury to military personnel and improving the safety and effectiveness of military operations.</li><li dir="ltr">Education:&nbsp;Articulated robots are increasingly being used in educational settings to teach students about robots and <a href="https://www.wevolver.com/article/understanding-industrial-automation-a-comprehensive-guide" target="_blank" rel="noopener noreferrer">robotic automation</a>. Robots are specifically programmed to perform a specific set of tasks, allowing students to learn about robotics in a hands-on, interactive way.</li></ol><h1 dir="ltr">Selection Criteria for Articulated Robots</h1>When it comes to selecting an&nbsp;industrial robot, there are a variety of factors to consider, including the application,&nbsp;payload&nbsp;capacity, reach, and&nbsp;repeatability&nbsp;required. In some cases, an&nbsp;articulated robot&nbsp;may be the best choice, while in others, a different&nbsp;type of robot, such as a&nbsp;Cartesian robot, a&nbsp;SCARA robot, or a&nbsp;Delta robot, may be more appropriate.Suggested Reading:<a href="https://www.wevolver.com/article/what-are-robot-gantries-types-applications-advantages-selection-criteria-and-more" target="_blank">&nbsp;What are Robot Gantries? Types, Applications, Advantages, Selection Criteria, and more</a>Articulated robots&nbsp;are often the preferred choice when a high degree of flexibility and maneuverability is required. These robots have multiple joints that can move independently, allowing them to reach a wide range of positions and orientations.The ability of&nbsp;articulated robots&nbsp;to move in multiple directions makes them well-suited for applications that require complex movements. They are also capable of handling a variety of&nbsp;payloads, from small electronic components to heavy&nbsp;automotive&nbsp;parts.In contrast,&nbsp;Cartesian robots&nbsp;are designed to move in a straight line along three axes, making them ideal for applications that require precise linear motion in a cubical&nbsp;work envelope. Another type of&nbsp;industrial robot&nbsp;is the&nbsp;SCARA robot, which is designed for tasks that require&nbsp;high-speed,&nbsp;precise movement&nbsp;in a horizontal plane.When deciding whether to use an&nbsp;articulated robot&nbsp;or another type of&nbsp;industrial robot, it is important to consider the specific requirements of the application.&nbsp;Articulated robots&nbsp;are best suited for applications that require a high degree of flexibility and maneuverability, while Cartesian and&nbsp;SCARA robots&nbsp;are better suited for applications that require precise linear or horizontal movement.Ultimately, the selection of an&nbsp;industrial robot&nbsp;will depend on the application's specific needs, as well as factors such as cost, ease of programming, and maintenance requirements. By carefully evaluating the application's requirements, it is possible to choose the right&nbsp;type of robot&nbsp;for the job and ensure maximum efficiency and productivity.<h1 dir="ltr">Future Developments in Articulated Robots</h1>As technology continues to evolve,&nbsp;articulated robots&nbsp;are expected to become even more advanced and versatile. Here are some of the developments that we can expect to see in the future of&nbsp;articulated robotics:<ol><li dir="ltr">Enhanced sensory capabilities:&nbsp;As sensor technology continues to improve,&nbsp;articulated robots&nbsp;will be able to sense and respond to their environment in more sophisticated ways. This will allow robots to adapt to changing environments and perform more complex tasks.</li><li dir="ltr">Improved mobility:&nbsp;Advances in mobility technology, such as the development of new types of actuators and locomotion systems, will allow&nbsp;articulated robots&nbsp;to move more efficiently and effectively in a wider range of environments.</li><li dir="ltr">Artificial intelligence:&nbsp;The integration of artificial intelligence and machine learning algorithms into&nbsp;articulated robots&nbsp;will enable them to learn and adapt to new tasks and environments more quickly and efficiently.</li><li dir="ltr">Collaborative robots:&nbsp;Collaborative robots, or "cobots," will become more prevalent in the future of&nbsp;articulated robotics. These robots are designed to work alongside human workers, performing tasks that are too dangerous, repetitive, or strenuous for humans to perform.</li><li dir="ltr">Miniaturization:&nbsp;Advances in miniaturization technology will allow for the development of smaller, more compact&nbsp;articulated robots&nbsp;that can perform tasks in tight spaces or delicate environments.</li></ol><h1 dir="ltr">Conclusion</h1>Articulated robots have revolutionized the field of <a href="https://www.wevolver.com/article/understanding-industrial-automation-a-comprehensive-guide" target="_blank" rel="noopener noreferrer">industrial automation</a>, offering greater flexibility, precision, and efficiency than traditional factory automation methods. These robots are used in various industries and applications, from manufacturing and healthcare to entertainment and agriculture.While there are limitations to the use of&nbsp;articulated robots, such as cost and programming complexity, their advantages make them an attractive option for businesses looking to improve their productivity and efficiency.Looking to the future, we can expect to see continued advancements in&nbsp;articulated robot&nbsp;technology, such as enhanced sensory capabilities, improved mobility, and the integration of artificial intelligence and machine learning algorithms. These developments will further increase the&nbsp;versatility&nbsp;and efficiency of&nbsp;articulated robots, allowing them to perform even more complex tasks in a wider range of environments.The future of&nbsp;articulated robotics&nbsp;is promising, and businesses that embrace this technology will likely see significant benefits in increased productivity, efficiency, and safety.

Articulated robots are highly versatile and efficient robots that have the ability to mimic human arm movements for various industrial and commercial applications.

What are Articulated Robots? Anatomy, Control Systems, Advantages, Selection Criteria, and Applications

With unmatched precision and adaptability, manipulator robots have become indispensable in numerous industries reliant on automation. This article outlines their design, components, classifications, applications, and the emerging trends influencing their role in the world of robotics.

What are manipulator robots? Understanding their Design, Types, and Applications

Delta arms have become an essential part of modern robotics and automation. These versatile machines are at the forefront of innovation, driving efficiency and precision in numerous industries. In this comprehensive guide, we will explore the fascinating world of delta arms, from their basic structure and components to their wide-ranging applications and future developments.<h2 dir="ltr">What is a Delta Arm?</h2>A delta arm, also known as a delta robot, is a type of parallel robot that features a unique, triangular base and lightweight, interconnected arms. These arms can move in all three dimensions and are attached to a central <a href="https://www.wevolver.com/article/end-effector" target="_blank" rel="noopener noreferrer">end effector</a>, which can be customized for various applications, such as gripping, cutting, or dispensing materials. Delta arms are known for their speed, precision, and flexibility, making them highly sought-after in industries like manufacturing, packaging, and assembly.<h2 dir="ltr">History of Delta Arms</h2>The delta arm was first invented in the early 1980s by Swiss engineer Reymond Clavel, who was inspired by the desire to create a high-speed, accurate robot for use in the watchmaking industry. Clavel's invention sparked a revolution in robotics, with companies and researchers around the world quickly adopting and refining delta arm technology. Notable companies like ABB, FANUC, and KUKA participated in the development of delta arms. Today, delta arms are used in a wide array of applications, from <a href="https://www.wevolver.com/article/understanding-industrial-automation-a-comprehensive-guide" target="_blank" rel="noopener noreferrer">industrial automation</a> to 3D printing and even medical procedures.<h2 dir="ltr">How Delta Arms Work?</h2>At the heart of the delta arm's impressive performance are its unique design and kinematics. Delta arms employ a parallel mechanism, which allows the arms to move simultaneously and in harmony with one another. This setup enables the end effector to maintain a consistent orientation while moving through its workspace, resulting in highly precise and accurate motion.To better understand delta arm kinematics, it's helpful to visualize the system as a series of interconnected links and joints. As the arms move, the joints rotate, causing the end effector to translate through space. By carefully controlling these rotations, the delta arm can achieve a wide range of positions and orientations with remarkable speed and precision.<iframe width="640" height="360" src="https://www.youtube.com/embed/3-NeyKcyfC4?&amp;ab_channel=ArturWiebe&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true" data-gtm-yt-inspected-9="true" id="840383707" title="#BottleCapChallenge" data-gtm-yt-inspected-23="true"></iframe> A prototype of a delta arm showcases its movements by precisely attaching a bottle cap and then removing it with ease.<h2 dir="ltr">Components of Delta Arms</h2><h3 dir="ltr">Base</h3>The base of a delta arm serves as the foundation for the entire system, providing stability and support for the arms and end effector. Bases come in various designs, but most commonly feature a triangular or circular shape. The material used in the base's construction can vary depending on the intended application, with common choices including aluminum, steel, and even 3D-printed materials for lighter-weight applications.<h3 dir="ltr">Arms</h3>The arms of a delta robot are crucial to its movement and overall performance. These slender, interconnected components enable the robot to move with great speed and agility. Arms can be constructed from a variety of materials, such as carbon fiber, aluminum, or steel, with the choice of material typically dependent on the required strength, weight, and cost considerations for the specific application.<h3 dir="ltr">Joints</h3>Joints are essential to the delta robot kinematics, providing the flexibility and range of motion necessary for the robot to function effectively in its working envelope while exhibiting good repeatability. There are several types of joints used in delta arms, including revolute, prismatic, and ball-and-socket joints. The selection of the appropriate joint type depends on the desired movement and performance characteristics for the specific delta arm application.<iframe width="640" height="360" src="https://www.youtube.com/embed/ih3oXigeY-U?&amp;ab_channel=KeyanGhazi-Zahedi&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true" data-gtm-yt-inspected-9="true" id="739495307" title="Prismatic Joint" data-gtm-yt-inspected-23="true"></iframe>A prismatic joint<h3 dir="ltr">End effector</h3>The end effector is the business end of a delta arm, responsible for carrying out the tasks the robot is designed to perform. End effectors can include grippers, cutters, welding tools, dispensers, vision systems, and more. Some delta arms even feature interchangeable end effectors, allowing them to be easily adapted for different tasks as needed.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjgwNTQyNjMzMTMwLTE2ODA1NDI2MzMxMzAucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" width="720" height="540" class="fr-fic fr-dii">Fig. 1: A delta arm robot with a customizable end effectorSuggested reading:&nbsp;<a href="https://www.wevolver.com/article/what-are-end-effectors-in-robotics-types-of-end-effectors-applications-future/" rel="noopener noreferrer" target="_blank">What are End Effectors in Robotics? Types of End Effectors, Applications, Future</a><h3 dir="ltr">Actuators</h3>Actuators are the driving force behind a delta arm's movement, converting electrical or pneumatic energy into mechanical motion. There are several types of actuators used in delta arms, including stepper motors, servo motors, and pneumatic cylinders. The choice of actuator depends on factors such as the required speed, precision, and force, as well as cost considerations for the specific application.Suggested reading:&nbsp;<a href="https://www.wevolver.com/article/what-is-an-actuator-principles-classification-and-applications" rel="noopener noreferrer" target="_blank">What is an Actuator? Principles, Classification, and Applications</a><h3 dir="ltr">Controllers</h3>Controllers play a crucial role in delta arm operation, managing the movement and coordination of the robot's various components. Controllers can range from simple microcontrollers to more advanced robot control systems incorporating artificial intelligence and machine learning capabilities. The selection of a controller depends on factors such as the complexity of the delta arm's movements, the desired level of precision, and the integration requirements with other systems or devices.<h2 dir="ltr">Applications of Delta Arms</h2><h3 dir="ltr">Industrial Automation</h3>Delta arms have become a staple in production line automation, with their speed, precision, and flexibility making them ideal for applications such as manufacturing, packaging, material handling,&nbsp;and assembly. In manufacturing, delta arms are used to pick and place components, assemble products, and even perform quality control checks. In packaging, they can quickly and accurately fill containers, apply labels, and seal packages. Additionally, delta arms have found a home in assembly lines, where their speed and dexterity make them perfect for tasks such as electronic component placement and assembly.Suggested Reading:&nbsp;<a href="https://www.wevolver.com/article/what-are-robotic-assembly-lines-history-components-advantages-limitations-applications-and-future" target="_blank">What are Robotic Assembly Lines? History, Components, Advantages, Limitations, Applications, and Future</a><h3 dir="ltr">3D Printing</h3>Delta arms have made a significant impact on the world of 3D printing, with their unique design and movement capabilities making them well-suited for this application. Delta arm-based 3D printers can achieve high levels of accuracy and resolution, while their lightweight arms allow for rapid movement and reduced print times.<h3 dir="ltr">Medical and Pharmaceutical Industries</h3>Delta arms are increasingly finding applications in the medical and pharmaceutical fields, with their precision and dexterity making them ideal for tasks such as drug discovery, surgery, and patient care. In drug discovery, delta arms can be used to quickly and accurately handle and analyze samples, speeding up the research process.&nbsp;In surgery, delta arms have been employed in procedures such as laparoscopy and microsurgery, where their precision and minimal invasiveness offer significant benefits.&nbsp;In patient care, delta arms can be used for tasks such as administering medications, drawing blood samples, and even assisting with rehabilitation exercises.<h3 dir="ltr">Food and Beverage Industry</h3>The food industry has also embraced delta arms for their speed, accuracy, and hygienic properties. Delta arms are used in various aspects of food processing, from sorting and grading fruits and vegetables to handling delicate baked goods. In packaging, delta arms can rapidly and accurately fill containers, apply labels, and seal packages, ensuring product quality and safety. Additionally, delta arms have been utilized in quality control tasks, such as inspecting food items for defects or contamination.<h3 dir="ltr">Other Applications</h3>While the aforementioned industries represent some of the most common applications for delta arms, these versatile robots have also found their way into many other fields. For example, delta arms have been used in research and development, aerospace, and entertainment industries, to name a few. As technology advances and delta arms become even more adaptable and efficient, their potential applications will undoubtedly continue to grow.<h2 dir="ltr">Choosing and Implementing Delta Arms</h2>When selecting a delta arm for a specific application, there are several factors to consider, such as payload capacity, degrees of freedom, speed, accuracy, and cost. Payload capacity refers to the maximum weight the delta arm can carry or manipulate, and it is essential to choose a delta arm with the appropriate capacity for the intended task. Degrees of freedom is nothing but the number of joints present in the robot that allow it to move in the x, y, and z-axis. Speed and accuracy are also vital considerations, as they will impact the overall performance and efficiency of the delta arm in its specific application. Finally, cost is always a factor, with the selection of materials, actuators, and controllers playing a significant role in determining the overall price of the delta arm.Suggested reading:&nbsp;<a href="https://www.wevolver.com/article/types-of-robots" target="_blank">7 Types of Industrial Robots: Advantages, Disadvantages, Applications, and More</a><h3 dir="ltr">Integration with Existing Systems</h3>Integrating a delta arm into an existing system can be a complex process, requiring careful planning and consideration. Factors such as compatibility with existing hardware and software, workspace constraints, and safety protocols must all be taken into account. Additionally, training employees to operate and maintain the delta arm is another critical aspect of successful integration.<h3 dir="ltr">Maintenance and Troubleshooting</h3>Regular maintenance is essential to keep a delta arm functioning optimally and ensure a long service life. This maintenance may include tasks such as lubricating joints, checking for wear and tear on components, and calibrating the system for optimal performance. Familiarity with common troubleshooting techniques is also crucial, as it allows operators to quickly diagnose and resolve issues that may arise during operation. Potential issues can include mechanical failures, software glitches, and problems with the end effector or other components.<h2 dir="ltr">The Future of Delta Arms</h2>Recent advancements in delta arm technology have focused on improving performance, efficiency, and versatility. Innovations such as lightweight materials, advanced actuators, and more sophisticated controllers have all contributed to the ongoing evolution of delta arms. As technology continues to advance, we can expect even more impressive capabilities from these remarkable machines.<iframe width="640" height="360" src="https://www.youtube.com/embed/yXNbG4P8fTU?&amp;t=37s&amp;ab_channel=ABBRobotics&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true" data-gtm-yt-inspected-9="true" id="421892765" title="IRB 365 FlexPicker® Delta Robot - lightweight picking, reorientating and packing" data-gtm-yt-inspected-23="true"></iframe>The ABB FlexPicker: a high-throughput variant of a delta robot arm suitable for rapid pick and place applications and more<h3 dir="ltr">The Role of AI in Delta Arms</h3>Artificial intelligence (AI) is increasingly being integrated with delta arms, allowing for enhanced performance and adaptability. AI-powered delta arms can learn from experience, adapting their movements and strategies to optimize efficiency and effectiveness. As AI continues to advance, we can expect even more exciting developments in the world of delta arm robotics.<h3 dir="ltr">The Impact of Delta Arms on the Workforce</h3>The rise of delta arms in various industries has raised questions about their potential impact on the workforce and job market. While it is true that delta arms can automate many tasks that humans once performed, they can also complement human labor rather than replace it entirely. By taking over repetitive, mundane, or dangerous tasks, delta arms can free up human workers to focus on more creative, strategic, and higher-value activities. Nevertheless, the integration of delta arms in the workforce also presents challenges, such as the need for retraining and upskilling workers to adapt to new technologies and roles. Addressing these challenges through effective education and training programs will be essential for ensuring a smooth transition and maximizing the benefits of delta arm technology.<h2 dir="ltr">Conclusion</h2>In conclusion, delta arms have made a significant impact on the world of robotics and automation, offering speed, precision, and flexibility that make them invaluable in a wide range of industries. From manufacturing and packaging to medical procedures and 3D printing, these versatile robots are revolutionizing the way we work and live. As technology continues to advance, we can expect even more exciting developments in the world of delta arms, making them an essential part of our future.<h2 dir="ltr">Frequently Asked Questions (FAQs)</h2><ol><li dir="ltr" style="font-weight: bold;">What are the main benefits of using delta arms in automation?</li></ol>Delta arms offer numerous advantages, such as speed, precision, flexibility, and cost-effectiveness, making them ideal for a wide range of automation tasks.<ol start="2"><li dir="ltr" style="font-weight: bold;">How do delta arms compare to other robotic arms in terms of performance?</li></ol>Delta arms are known for their unique combination of speed, precision, and agility, which sets them apart from other types of robotic arms.<ol start="3"><li dir="ltr" style="font-weight: bold;">Can delta arms be used in hazardous environments?</li></ol>With appropriate modifications and precautions, delta arms can be used in hazardous environments, such as those involving high temperatures, radiation, or corrosive substances.<ol start="4"><li dir="ltr" style="font-weight: bold;">What are the main challenges in implementing delta arms in existing systems?</li></ol>Potential challenges include integration with existing hardware and software, workspace constraints, safety protocols, and training employees to operate and maintain the delta arm.<ol start="5"><li dir="ltr" style="font-weight: bold;">How much do delta arms typically cost?</li></ol>The cost of a delta arm can vary widely, depending on factors such as size, capabilities, and materials used. Prices can range from a few thousand dollars for basic models to tens or even hundreds of thousands of dollars for more advanced systems.<ol start="6"><li dir="ltr" style="font-weight: bold;">What is the expected lifespan of a delta arm?</li></ol>The average lifespan of a delta arm depends on factors such as usage, maintenance, and build quality. With proper care, a delta arm can typically last for many years or even decades.<ol start="7"><li dir="ltr" style="font-weight: bold;">Are delta arms safe to use alongside human workers?</li></ol>With appropriate safety measures and best practices in place, delta arms can be safely used in collaborative work environments, complementing human labor and improving overall efficiency.<h2 dir="ltr">References</h2>[1] Delta robot. (2023, March 7). In Wikipedia. <a data-fr-linked="true" href="https://en.wikipedia.org/wiki/Delta_robot" id="isPasted" rel="noopener noreferrer" target="_blank">https://en.wikipedia.org/wiki/Delta_robot</a>[2] Pierrot F, Reynaud C, Fournier A. DELTA: a simple and efficient parallel robot. Robotica. Cambridge University Press; 1990;8(2):105–9.&nbsp;

Delta arms are high-performance robotic arms that use parallel linkages and jointed construction to provide fast, precise, and flexible movement, making them ideal for various industrial and manufacturing applications.

The Ultimate Guide to Delta Arms: Revolutionizing Robotics and Automation

Robotic joints, which are sometimes known as axes, are the moveable parts of a robot that cause relative motion between adjacent links. These links refer to the rigid components that connect the joints to ensure their proper and straightforward operation. 

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Robotic Arms

Profiles