From November 12 to 15, Munich became the epicenter of the global electronics industry as Electronica 2024 brought together 3,480 exhibitors and nearly 80,000 visitors from across the world. Renowned as the industry’s leading trade fair, this year’s event was more international than ever, offering a glimpse into how digital technologies are shaping the path to a carbon-neutral future. Spanning 18 exhibition halls, the event highlighted groundbreaking advancements in artificial intelligence (AI), sustainability, and the future of mobility, alongside the critical need to foster young talent in electronics.<h2>Key Topics Shaping the Electronics Landscape</h2><h3>Sustainability: Electronics Driving the Green Revolution</h3>At the heart of this year’s event was the drive to achieve carbon neutrality, with many exhibitors showcasing how electronics play a crucial role in environmental sustainability. From energy-efficient semiconductor solutions to IoT technologies that enable smarter energy management, it was clear that the industry is stepping up to address climate challenges.Sustainability discussions centered on:<ul><li>Smart Grids and Renewable Energy: Innovations in optimizing energy systems for greater efficiency.</li><li>Circular Economy: Efforts to design electronics for recyclability and longevity.</li><li>Green Electronics Manufacturing: Reducing the environmental footprint of production processes.</li></ul><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1732193109394-electronica-infineon.jpg" style="width: 100%px;" class="fr-fic fr-dib">Smart and sustainable mobility was one of the main topics at this year's Electronica.<h3>Artificial Intelligence: Powering the Industry</h3>AI continued to dominate conversations, underscoring its influence across sectors. At Electronica 2024, AI’s transformative power was evident in:<ul><li>Smart Manufacturing: Machine learning-driven solutions to optimize production lines and reduce waste.</li><li>AI-powered Sensors: New sensor technologies with AI integration for predictive analytics in industrial and healthcare applications.</li><li>Autonomous Mobility: The role of AI in enhancing vehicle safety and energy efficiency.</li></ul><h3>Future of Mobility: Shifting Gears with Electronics</h3>The push toward sustainable transportation took center stage, with electronics enabling electric vehicles (EVs), autonomous systems, and advanced battery technologies. Key themes included:<ul><li>Electrification: Efficient power management and battery advancements for EVs.</li><li>Connectivity: V2X (vehicle-to-everything) communication for smarter traffic ecosystems.</li><li>Safety Systems: Innovations in sensor fusion and AI-enhanced driver-assistance technologies.</li></ul><h3>Fostering Talent: Investing in the Future</h3>With the global electronics industry facing a talent shortage, Electronica 2024 placed special emphasis on nurturing the next generation of engineers and innovators. Events, workshops, and mentorship programs were tailored to inspire students and young professionals to take up roles in this rapidly evolving field.<h2>Exhibitor Highlights: Innovations to Watch</h2><h3>Infineon Technologies: AI and Sustainability at the Core</h3><a href="https://www.infineon.com/cms/en/product/promopages/electronica/" target="_blank" rel="noopener noreferrer">Infineon</a> stole the spotlight with its dual focus on sustainability and digitalization. Showcasing AI-enabled semiconductor solutions, the company emphasized its commitment to green energy and smart mobility. Their technologies are pivotal in advancing applications such as solar energy systems, electric drives, and secure IoT solutions.<h3>Altium: Transforming Electronics Design</h3><a href="https://www.messe.tv/en/2024/electronica/altium-life-cycle-electronics-development" target="_blank" rel="noopener noreferrer">Altium</a>, a multinational software company specializing in electronic design automation, unveiled a trifecta of solutions—Altium Discover, Altium&nbsp;Develop, and Altium Lifecycle—aimed at streamlining the electronics design process. From conceptualization to product lifecycle management, these tools empower engineers to innovate faster and with greater efficiency.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1732192928529-novelties_altium_booth_electronica_2024_fair_munich-large.webp" style="width: 100%px;" class="fr-fic fr-dib">Altium presented their three new solutions for solutions for the entire electronics development life cycle: Discover, Develop and Lifecycle. Source:&nbsp;<a href="https://www.messe.tv/en/2024/electronica/altium-life-cycle-electronics-development" target="_blank" rel="noopener noreferrer">Altium</a><h3>Nordic Semiconductor: The New Standard in Bluetooth LE</h3>Nordic Semiconductor introduced the <a href="https://www.nordicsemi.com/Nordic-news/2023/10/Nordic-announces-nRF54L-Series-expanding-industrys-most-efficient-Bluetooth-LE-portfolio" target="_blank" rel="noopener noreferrer">nRF54L Series,</a> expanding the world’s most energy-efficient Bluetooth Low Energy (LE) portfolio. With a focus on ultra-low power consumption, this series is ideal for applications like wearables, medical devices, and IoT solutions, pushing the boundaries of connectivity.<h3>Analog Devices: Agile Manufacturing Robotics</h3><a href="https://www.analog.com/en/lp/001/electronica.html" target="_blank" rel="noopener noreferrer">Analog Devices</a> showcased robotics innovations for agile manufacturing, demonstrating how precision sensors and edge computing can revolutionize production lines. Their solutions highlighted the growing importance of flexibility and efficiency in Industry 4.0 environments.<h3>Ignion: IoT Innovation</h3>Ignion focused on smart antenna solutions designed to improve IoT device performance, showcasing their new <a href="https://ignion.io/landing-page/discover-omnia-mxtend/" target="_blank" rel="noopener noreferrer">OMNIA mXTEND™</a>, a one-of-a-kind three-port Virtual Antenna® component designed to simplify RF connectivity for IoT applications.&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1732194027984-ignion-omnia-mxtend-antenna.jpg" style="width: 100%px;" class="fr-fic fr-dib">Ignion unveiled their new OMNIA mXTEND™ Virtual Antenna.&nbsp;<h2>From Carbon Goals to Connectivity: Electronica’s Blueprint for the Future</h2>Electronica 2024 left attendees with a clear message: the future of electronics is deeply intertwined with global efforts to create a sustainable, connected, and intelligent world. Innovations showcased at the event demonstrated the industry’s resilience and ability to address pressing challenges, from climate change to supply chain disruptions.As we look ahead, several trends stand out:<ol><li>AI as the Catalyst: Expect deeper integration of AI across all electronics applications, driving efficiency and unlocking new capabilities.</li><li>Sustainability as a Mandate: Green manufacturing and circular design will become non-negotiable for businesses aiming to stay competitive.</li><li>Talent Development: The need for a robust talent pipeline will see continued investment in education and mentorship.</li></ol>For engineers, technologists, and industry stakeholders, Electronica 2024 served as both a showcase of current advancements and a roadmap for the exciting developments yet to come. As the industry evolves, platforms like Electronica will remain indispensable for fostering collaboration, innovation, and progress.

Over 80,000 visitors and 3,400 exhibitors attended this year's Electronica in Munich. Image source: Electronica.

From AI-powered manufacturing to sustainable mobility solutions, Electronica 2024 revealed how electronics are paving the way to a carbon-neutral world. This year’s record-breaking event featured game-changing technologies and fostered collaboration across the global industry.

Electronica 2024: Key Insights and Innovations Driving the Electronics Industry Forward

For roboticists, one challenge towers above all others: generalization – the ability to create machines that can adapt to any environment or condition. Since the 1970s, the field has evolved from writing sophisticated programs to using deep learning, teaching robots to learn directly from human behavior. But a critical bottleneck remains: data quality. To improve, robots need to encounter scenarios that push the boundaries of their capabilities, operating at the edge of their mastery. This process traditionally requires human oversight, with operators carefully challenging robots to expand their abilities. As robots become more sophisticated, this hands-on approach hits a scaling problem: the demand for high-quality training data far outpaces humans' ability to provide it.Now, a team of MIT CSAIL researchers have developed a novel approach to robot training that could significantly accelerate the deployment of adaptable, intelligent machines in real-world environments. The new system, called "LucidSim," uses recent advances in generative AI and physics simulators to create diverse and realistic virtual training environments, helping robots achieve expert-level performance in difficult tasks without any real-world data.<iframe width="640" height="360" src="https://www.youtube.com/embed/fgnJQrvTj70?&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"></iframe>LucidSim combines physics simulation with generative AI models, addressing one of the most persistent challenges in robotics: transferring skills learned in simulation to the real world. “A fundamental challenge in robot learning has long been the ‘sim-to-real gap’ – the disparity between simulated training environments and the complex, unpredictable real world,” says MIT CSAIL postdoctoral associate Ge Yang, a lead researcher on LucidSim. “Previous approaches often relied on depth sensors, which simplified the problem but missed crucial real-world complexities.”The multipronged system is a blend of different technologies. At its core, LucidSim uses large language models to generate various structured descriptions of environments. These descriptions are then transformed into images using generative models. To ensure that these images reflect real-world physics, an underlying physics simulator is used to guide the generation process.&nbsp;<h2>The Birth of an Idea: From Burritos to Breakthroughs</h2>The inspiration for LucidSim came from an unexpected place: a conversation outside Beantown Taqueria in Cambridge. ”We wanted to teach vision-equipped robots how to improve using human feedback. &nbsp;But then, we realized we didn't have a pure vision-based policy to begin with,” says Alan Yu, an undergraduate student at MIT and co-lead on LucidSim. "We kept talking about it as we walked down the street, and then we stopped outside the taqueria for about half an hour. That's where we had our moment."To cook up their data, the team generated realistic images by extracting depth maps, which provide geometric information, and semantic masks, which label different parts of an image, from the simulated scene. They quickly realized, however, that with tight control on the composition of the image content, the model would produce similar images that weren’t different from each other using the same prompt. So, they devised a way to source diverse text prompts from ChatGPT.&nbsp;This approach, however, only resulted in a single image. To make short, coherent videos which serve as little “experiences” for the robot, the scientists hacked together some image magic into another novel technique the team created, called “Dreams In Motion (DIM).” The system computes the movements of each pixel between frames, to warp a single generated image into a short, multi-frame video. Dreams In Motion does this by considering the 3D geometry of the scene and the relative changes in the robot’s perspective.&nbsp;"We outperform domain randomization, a method developed in 2017 that applies random colors and patterns to objects in the environment, which is still considered the go-to method these days," says Yu. &nbsp;"While this technique generates diverse data, it lacks realism. LucidSim addresses both diversity and realism problems. It’s exciting that even without seeing the real world during training, the robot can recognize and navigate obstacles in real environments."&nbsp;The team is particularly excited about the potential of applying LucidSim to domains outside quadruped locomotion and parkour, their main testbed. One example is mobile manipulation, where a mobile robot is tasked to handle objects in an open area, and also, color perception is critical. “Today, these robots still learn from real-world demonstrations,” says Yang. “Although collecting demonstrations is easy, scaling a real-world robot teleoperation setup to thousands of skills is challenging because a human has to physically set up each scene. We hope to make this easier, thus qualitatively more scalable, by moving data collection into a virtual environment.”&nbsp;The team put LucidSim to the test against an alternative, where an expert teacher demonstrates the skill for the robot to learn from. The results were surprising: robots trained by the expert struggled, succeeding only 15 percent of the time – and even quadrupling the amount of expert training data barely moved the needle. But when robots collected their own training data through LucidSim, the story changed dramatically. Just doubling the dataset size catapulted success rates to 88 percent. "And giving our robot more data monotonically improves its performance – eventually, the student becomes the expert," says Yang.“One of the main challenges in sim-to-real transfer for robotics is achieving visual realism in simulated environments,” says Stanford University Assistant Professor of Electrical Engineering Shuran Song, who wasn’t involved in the research. “The LucidSim framework provides an elegant solution by using generative models to create diverse, highly realistic visual data for any simulation. This work could significantly accelerate the deployment of robots trained in virtual environments to real-world tasks.”From the streets of Cambridge to the cutting edge of robotics research, LucidSim is paving the way toward a new generation of intelligent, adaptable machines – ones that learn to navigate our complex world without ever setting foot in it.<hr>Yu and Yang wrote the paper with four fellow CSAIL affiliates: mechanical engineering postdoc Ran Choi; undergraduate researcher Yajvan Ravan; John Leonard, Samuel C. Collins Professor of Mechanical and Ocean Engineering in the MIT Department of Mechanical Engineering; and MIT Associate Professor Phillip Isola. Their work was supported, in part, by a Packard Fellowship, a Sloan Research Fellowship, the Office of Naval Research, Singapore’s Defence Science and Technology Agency, Amazon, MIT Lincoln Laboratory, and the National Science Foundation Institute for Artificial Intelligence and Fundamental Interactions. The researchers will present their work at the Conference on Robot Learning (CoRL) in early November.

A team of MIT CSAIL researchers have developed a novel approach to robot training that could significantly accelerate the deployment of adaptable, intelligent machines in real-world environments.

Can robots learn from machine dreams?

In a world where automation dominates the manufacturing sector, one question persists: why aren't robots omnipresent in our daily lives? The answer lies in the intricate challenges of bridging the gap between humans and machines. While robots thrive in structured environments, the unpredictability and complexity of human behavior pose significant hurdles to seamless integration.&nbsp;In Robotican's recent work, we have developed a new way for humans to interact with service robots. A person is talking to a robot and command it to perform series of tasks a spoken language. Such a command can be: "Go forward up until the end of the hallway, enter the room in front of you, ask the doctor to give you the examination files, and come back here". The robot uses a Speech-To-Text engine, to write the command in text, then it applies a specially trained LLM (Large Language Model) to divide the command into sub-tasks. In our example the sub-tasks will be: "move forward, enter the room, ask the doctor, go back".&nbsp; The robot start performing the sub-tasks according to the required order. &nbsp;While moving the robot uses it cameras to "see" the environment in front of it. The frame from this camera goes into an LVM (Large Visual Model) which receives a photo and output text. When the way is clear this LVM will output "move forward", and an action will be: send the robot a navigation way point 10 meters ahead". When say, in our example, a group of people are standing in front of the robot blocking the way. Then the LVM will output "People are blocking the way", and the action will be "ask them to let me pass through". &nbsp;Then a Text-To-Speech engine will say "Excuse me, I'm a robot with an important task. May I please pass through? Thank you.” Then the robot pass and continues its task. This process is repeated until the robot complete its task, or is unable to do it. <img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1731928237835-%D7%AA%D7%9E%D7%95%D7%A0%D7%94+2+%D7%9C%D7%9E%D7%90%D7%9E%D7%A8+2+%D7%95%D7%95%D7%99%D7%91%D7%95%D7%9C%D7%91%D7%A8.jpg" style="width: 100%px;" class="fr-fic fr-dib">"The answer lies in the intricate challenges of bridging the gap between humans and machines" This is a unique approach of combining TTS, STT, LLM and LVM to allow robot interact with human, and human interact with a robot in a spoken language, while understanding tasks, seeing and understanding the environment around it, and asking people to help the robot conducting its task successfully. Enter Robotican, a pioneering force at the forefront of autonomous robotics, spearheading the charge to revolutionize Human-Robot Interfaces. As a key player in the Israel Innovation Authority's groundbreaking consortium, Robotican harnesses its extensive expertise and recent advancement in AI and Deep Learning to develop innovative solutions that allows robots to navigate amongst people while interacting with them similarly to the way other peoples do.&nbsp;Championing the quest to teach robots to navigate unstructured environments effortlessly, Robotican embarks on a transformative journey empowered by cutting-edge Large Language and Vision Models (LLM &amp; LVM). Leveraging the revolutionary capabilities of these models, Robotican engineers have unlocked the potential to communicate with robots in a language they understand.&nbsp;Omri Eitan, Robotican's AI expert, sheds light on this groundbreaking advancement, stating, "The transformative power of the latest Language Model technology has ushered in a new era of complexity in decision-making and deep contextual reasoning. Tasks once deemed intensive engineering endeavors now undergo fine-tuning, and interactions once artificial now feel natural." &nbsp; With Robotican's innovative approach, robots decode textual or verbal instructions, process visual data using LVM to navigate crowded environments, and seamlessly engage in dialogue with individuals, courtesy of LLM. This paradigm shift not only enhances precision in task execution but also fosters meaningful human-like interactions, paving the way for unprecedented advancements in automation and artificial intelligence.&nbsp;Witness the dawn of a new era as Robotican redefines the landscape of human-robot interaction, propelling automation into realms previously deemed unimaginable. Join us as we embark on this transformative journey, shaping a future where robots seamlessly integrate into every facet of our lives. #RoboticanRevolution #HumanRobotInteraction #AIInnovation #AutomationAdvancements

Ghostrobotics V60 with Robotican's ARK (autonomous robotic kit)  

Pioneering AI-Driven Collaboration: Transforming Social Navigation into daily routine

Unveiling the Future: Human-Robot Interaction Revolutionizes Social Navigation Using AI and Deep Learning

Point rate 64000 points/s&nbsp;Distance resolution 4.5mm&nbsp;FOV 360°x96°&nbsp;Scan distance 30m&nbsp;Performance increased by 200%&nbsp;Open source address: <a dir="ltr" href="https://t.co/u1Hr2wB1T2" rel="noopener noreferrer nofollow" target="_blank" id="isPasted">https://github.com/unitreerobotics/point_lio_unilidar…</a>

New Upgrade Released: 200% Increase in L2 4D LiDAR Performance $419 🥳


New Upgrade Released: L2 4D LiDAR Doubles Performance for 419 USD

A less wasteful way to train large language models, such as the GPT series, finishes in the same amount of time for up to 30% less energy, according to a new study from the University of Michigan.The approach could save enough energy to power <a href="https://www.eia.gov/energyexplained/use-of-energy/electricity-use-in-homes.php" target="_blank" rel="noreferrer noopener">1.1 million U.S. homes</a> in 2026, based on Wells Fargo’s projections of <a href="https://www.marketwatch.com/story/ai-could-demand-a-shocking-amount-of-electricity-check-out-this-chart-e91e306d" target="_blank" rel="noreferrer noopener">AI power demand</a>. It could also take a bite out of the International Monetary Fund’s prediction that data centers could account for <a href="https://www.imf.org/en/Blogs/Articles/2024/08/15/carbon-emissions-from-ai-and-crypto-are-surging-and-tax-policy-can-help" target="_blank" rel="noreferrer noopener">1.2% of the world’s carbon emissions by 2027</a>—and the <a href="https://theconversation.com/ais-excessive-water-consumption-threatens-to-drown-out-its-environmental-contributions-225854">water demands</a> that come with that energy use.Some experts say that these costs could be outweighed by environmental benefits. They argue that AI could be a <a href="https://news.un.org/en/story/2023/11/1143187" target="_blank" rel="noreferrer noopener">“game changer” for fighting climate change</a> by identifying ways to optimize supply chains and the grid, manage our energy needs, and improve research on climate change. Still, that doesn’t excuse squandering energy, and some of the power used to train AI has zero impact on training time and model accuracy.“Why spend something when there’s no point?” said <a href="https://eecs.engin.umich.edu/people/chowdhury-mosharaf/" target="_blank" rel="noreferrer noopener">Mosharaf Chowdhury</a>, an associate professor of computer science and engineering and the corresponding author of the study presented at the <a href="https://sigops.org/s/conferences/sosp/2024/index.html" target="_blank" rel="noreferrer noopener">30th Symposium on Operating Systems Principles</a> last Monday.“We can’t keep building bigger and bigger data centers because we won’t have the power to run them,” said Chowdhury. “If we can reduce the energy consumed by AI, we can reduce AI’s carbon footprint and cooling requirements and allow for more computation to fit within our current energy constraints.”The energy waste is created when AI training is unequally divided between GPUs, which are computer processors specialized for large data and graphics applications. Although it opens the door for waste, splitting the work is necessary for processing huge datasets.“AI models today are so large, they cannot fit inside a single computer processor,” said <a href="https://jaewonchung.me/about" target="_blank" rel="noreferrer noopener">Jae-Won Chung</a>, a doctoral student in computer science and engineering and the first author of the study. “They need to be divided into tens of thousands of processors to be trained, but dividing the models in perfectly equal sizes across all processors is practically impossible.”The training jobs are so difficult to evenly split up because some tasks need to be grouped together on the same processor—like how each installment of a book series will be grouped together in an organized shelf. Depending on how the tasks are grouped, some processors might get stuck with the AI-training equivalent of the Encyclopedia Britannica while others get assigned a fantasy trilogy.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1731630851359-AI_waste_Infographic-768x1470.jpg" style="width: 100%px;" class="fr-fic fr-dib">Infographic credit: Rachel Voigt, Michigan Engineering.Because current training methods run each processor at top speed, processors with a lighter load will finish their calculations before other processors. This doesn’t speed up training, which isn’t complete until every processor finishes its job—but it is wasteful because faster calculations require more energy. In addition, problems such as faulty hardware or network delays create energy waste by slowing down a single processor’s computing speed.To save energy, the researchers developed a software tool, called&nbsp;<a href="https://ml.energy/zeus/research_overview/perseus/" target="_blank" rel="noreferrer noopener">Perseus</a>, that identifies a critical path, or a series of subtasks that will take the longest time to complete. Then, Perseus slows down processors that aren’t on the critical path so that they all finish their jobs around the same time—eliminating unnecessary power use.“Reducing the power cost of AI can have important implications for equitable AI access,” said Chowdhury. “If a country doesn’t have enough power to run a big model, they might need to use services from far away, or be stuck running smaller, less accurate models. This gap could further perpetuate disparity between different communities.”The team tested Perseus by training GPT-3, three other large language models and one computer vision model.Perseus is an open-sourced tool available as part of&nbsp;<a href="https://ml.energy/zeus/" target="_blank" rel="noreferrer noopener">Zeus</a>, a tool for measuring and optimizing AI energy consumption.&nbsp;

Smarter use of processor speeds saves energy without compromising training speed and performance.

Up to 30 percent of the power used to train AI is wasted. Here's how to fix it.

In this episode, we explore how the mechanics of bird wings are inspiring new approaches to prevent airplanes from stalling and learn how bio-mimetic designs from nature are paving the way for innovations in aviation, enhancing stability and safety for future flights.

Podcast: Scared of Flying Boeing? Nature Might Be The Solution

In order to promote the development of the global embodied AI industry, the Unitree G1 robot operation data set is open sourced, adapted to a variety of open source solutions, and continuously updated:&nbsp;Open source data collection:&nbsp;<a tabindex="0" href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqa3VhODBhSF8tQzQtdFlpaUk2blczM2t1WFdkd3xBQ3Jtc0ttTkF6bzFrSzVTbkk5elFNaXl4ZmZyQTE5VGFfbWpESlhxVS1VRklKVXozX2JrZ2ItekNReGx3Vm5QOHY2OGxER0diUUhhTWlBdmotSU9LUV9EcWV0dC00Y3pjTWpyQXlQUTU5RmZCYjhoRl9KeXhadw&q=https%3A%2F%2Fgithub.com%2Funitreerobotics%2Favp_teleoperate&v=OTWHXTu09wE" rel="nofollow" target="_blank" data-immersive-translate-walked="dc553b62-3d51-4d74-bb21-9b5c19549e6f">https://github.com/unitreerobotics/av...</a>Open source learning algorithms:&nbsp;<a tabindex="0" href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbFVfRV9DS25fNFpSMXVpaEJicUFBV29QY2NYd3xBQ3Jtc0tuejRtek9OWDF5Qk9Fc0FRNUhzWkl0SEdDSFM4UFRHLWk2MHdOUjR3LWxwN0JlUmxGRkJ5TjhlbE83S0hlZEhDY0IxQmFiOGJaM0MxRFVtdmtta3ZMMWVCOXlQMS1WR2hJa0hla0RtVHpvZWlQc3pPZw&q=https%3A%2F%2Fgithub.com%2Funitreerobotics%2Funitree_IL_lerobot&v=OTWHXTu09wE" rel="nofollow" target="_blank" data-immersive-translate-walked="dc553b62-3d51-4d74-bb21-9b5c19549e6f">https://github.com/unitreerobotics/un...</a>Open source datasets and models:&nbsp;<a tabindex="0" href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbGh0clVCMHBNbkpPWTczdTFjSGtWNGFfLVowUXxBQ3Jtc0trV2VfWFBrLUVNNS1oM3JNR2ZEU0d0MnlYZXhBNDVQVjNfYkN1aGd1b3BMc3JHUGtNTnFBN0JSMi1CQUJHSnRUTzZMTWNIZnJNQ29IUmNtT2t1blZkRXJ3ZEtfZmRKYVdqUGk1OWNZSUl4d1g5Qk16MA&q=https%3A%2F%2Fhuggingface.co%2FUnitreeRobotics&v=OTWHXTu09wE" rel="nofollow" target="_blank" data-immersive-translate-walked="dc553b62-3d51-4d74-bb21-9b5c19549e6f">https://huggingface.co/UnitreeRobotics</a>

In order to promote the development of the global embodied AI industry, the Unitree G1 robot operation data set is open sourced, adapted to a variety of open source solutions, and continuously updated.

Unitree G1 Open Source Dataset

<a href="https://www.projectceti.org/" target="_blank">Project CETI</a> (Cetacean Translation Initiative) aims to collect millions to billions of high-quality, highly contextualized vocalizations in order to understand how sperm whales communicate. But finding the whales and knowing where they will surface to capture the data is challenging — making it difficult to attach listening devices and collect visual information.&nbsp;Today, a Project CETI research team led by&nbsp;<a href="https://gil.seas.harvard.edu/" target="_blank">Stephanie Gil</a>, Assistant Professor of Computer Science at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), have proposed a new reinforcement learning framework with autonomous drones to find sperm whales and predict where they will surface.&nbsp;The research is published in&nbsp;<a href="https://www.science.org/doi/10.1126/scirobotics.adn7299" target="_blank">Science Robotics.&nbsp;</a>This new study uses various sensing devices, such as Project CETI aerial drones with very high frequency (VHF) signal sensing capability that leverage signal phase along with the drone’s motion to emulate an ‘antenna array in air’ for estimating directionality of received pings from CETI’s on-whale tags. It demonstrates that it’s possible to predict when and where a whale may surface by using these various sensor data as well as predictive models of sperm whales dive behavior. With that information, Project CETI can now design algorithms for the most efficient route for a drone to rendezvous—or encounter—a whale at the surface. This also opens up possible conservation applications to help ships avoid striking whales while at the surface.Presenting the Autonomous Vehicles for whAle Tracking And Rendezvous by remote Sensing, or AVATARS framework, this study jointly develops two interrelated components of autonomy and sensing: autonomy, which determines the positioning commands of the autonomous robots to maximize visual whale encounters; and sensing, which measures the Angle-of-Arrival (AOA) from whale tags to inform the decision-making process. Measurements from our autonomous drone to surfaced tags, acoustic AOA from existing underwater sensors, and whale motion models from previous biological studies of sperm whales are provided as inputs to the AVATARS autonomous decision-making algorithm, which in turn aims to minimize missed rendezvous opportunities with whales.&nbsp;AVATARS is the first co-development of VHF sensing and reinforcement learning decision-making for maximizing rendezvous of robots and whales at sea. A well-known application of time-critical rendezvous is used with rideshare apps, which uses real-time sensing to note the dynamic paths and positions of drivers and potential riders. When a rider requests a ride, it can assign a driver to rendezvous with the rider as efficiently and as timely as possible. Project CETI’s case is similar in that they are real-time tracking the whale, with the goal of coordinating the drone’s rendezvous to meet the whale at the surface.&nbsp;This research advances Project CETI's goal of obtaining millions to billions of high-quality, highly contextualized whale vocalizations. The addition of diverse types of data will improve location estimates and routing algorithms—helping Project CETI meet that goal more efficiently.“I’m excited to contribute to this breakthrough for Project CETI. By leveraging autonomous systems and advanced sensor integration, we’re able to solve key challenges in tracking and studying whales in their natural habitats. This is not only a technological advancement, but also a critical step in helping us understand the complex communications and behaviors of these creatures,” said Gil. “This research is a major milestone for Project CETI’s mission. We can now significantly enhance our ability to gather high-quality and large-scale dataset on whale vocalizations and the associated behavioral context, putting us one step closer to better listening to and translating what sperm whales are saying,” said David Gruber, Founder and Lead of Project CETI.“'This research was an amazing opportunity to test our systems and algorithms in a challenging marine environment. This interdisciplinary work, that combines wireless sensing, artificial intelligence and marine biology, is a prime example of how robotics can be part of the solution for further deciphering the social behavior of sperm whales,” said&nbsp;Ninad Jadhav, Harvard University PhD candidate and first author on the paper.“This project provides an excellent opportunity to test our algorithms in the field, where robotics and artificial intelligence can enrich data collection and expedite research for broader science in language processing and marine biology, ultimately protecting the health and habitat of sperm whales,” said Sushmita Bhattacharya, a postdoctoral researcher in Gil's REACT Lab at SEAS.

Project CETI and Harvard have established a new reinforcement learning framework for rendezvous with whales using autonomous robots, combining sensing from diverse sensor streams

New methods for whale tracking and rendezvous using autonomous robots

An ETH Zurich team of engineers has developed RoBoa – a snake-like robot with the ability to grow in length and slither into the most inaccessible places.

The snake that saves lives

operator piloting underwater ROV on Deep Trekker handheld controller

Discover the essential role of underwater positioning, and how remotely operated vehicles are advancing accuracy in underwater inspections and surveys.

Underwater Positioning 101: Navigating the Depths with ROVs

This article explores the principles behind optical encoders, their types, and their advantages, highlighting how they enhance performance, ensure accuracy, and meet demanding standards in complex control environments.

Optical Encoder Technology: Advanced Guide to Precision Motion Control Systems

Manufacturing defect rates have risen with metal fabrication facilities facing unprecedented challenges in maintaining quality control amid increasing operational complexity.&nbsp;As production lines become more sophisticated and quality standards more stringent, traditional inspection methods must be revised for modern manufacturing demands.Matroid is transforming this landscape by democratizing computer vision technology, making advanced automation accessible to industry professionals without coding expertise. Through its innovative platform, Matroid enables manufacturing teams to implement sophisticated defect detection and process monitoring solutions that can be deployed within minutes rather than months.The impact is significant in welding applications, where Matroid's computer vision technology can evaluate multiple quality parameters simultaneously - from porosity and spatter to burn-through - with the precision of a weld engineering expert.&nbsp;This scalable approach to quality control is proving essential as manufacturers face mounting pressure to maintain precision while managing costs.We will explore in detail how Matroid's computer vision technology is changing the landscape of metal manufacturing, examining its technical capabilities, practical applications, and the tangible benefits it brings to production environments. &nbsp;For decision-makers, we'll look at how this technology sets new standards in automated quality control and process optimization.<h2 dir="ltr">Industry Challenges and Opportunities in Metal Manufacturing</h2>The metal manufacturing industry faces unprecedented pressures, with many organizations' quality control costs consuming <a href="https://www.weboccult.com/blog/computer-vision-for-quality-control-in-manufacturing-a-comprehensive-guide">15-20%</a> of annual sales revenue. Traditional manual inspection methods, while common, are increasingly inadequate for modern manufacturing demands, showing inconsistency rates increase due to operator fatigue and subjective assessments.According to the US. Census Bureau <a href="https://www.census.gov/library/stories/2021/09/manufacturing-continues-to-be-among-top-five-largest-employment-sectors.html">study</a> shows that production facilities struggle with a critical shortage of skilled workers, with nearly one-fourth of the manufacturing workforce aged 55 or older. This demographic shift and early retirement trends post-pandemic have created significant gaps in quality control expertise and operational efficiency.<h3 dir="ltr">The Role of Automation and Computer Vision</h3>Computer vision technology is emerging as a transformative solution, capable of analyzing images and video from any imaging technology, spanning resolutions from low (VGA) to ultra-high (gigapixel) and across various spectra, including visible light, infrared, and x-ray. &nbsp;This enables the detection of the smallest and most challenging defects in manufacturing processes, enhancing quality control and operational efficiency.Unlike traditional rule-based inspection systems, modern computer vision &amp; AI solutions can detect and classify multiple defect types simultaneously, from surface imperfections to welding anomalies.Automated inspection systems are valuable in welding applications, where they can evaluate multiple quality parameters - including porosity, spatter, burn-through, edge welds, and cold welds - with consistent precision.&nbsp;Computer vision &amp; AI address the skilled labor shortage and significantly improve inspection accuracy, with some implementations showing up to <a href="https://cloud.google.com/blog/products/ai-machine-learning/improve-manufacturing-quality-control-with-visual-inspection-ai">10x</a> improvement in defect detection compared to conventional methods, which leads to dramatic cost savings in cost per part and rework efforts and also improves production throughput.<h2 dir="ltr">Matroid's Vision and Technological Focus</h2>Founded in 2016 by Stanford professor and founding member of Databricks Reza Zadeh, Matroid has established itself as a leader in enterprise-level computer vision &amp; AI software platform, backed by <a href="https://growjo.com/company/Matroid">$33.5M</a> in funding from prominent investors, including NEA, Energize Ventures, and Intel Capital.&nbsp;The Matroid <a href="https://www.matroid.com/">platform</a> domain experts from manufacturing to rapidly deploy and adapt custom detectors that can leverage camera systems' imagery/ video types to detect and classify defects, monitor manual assembly operations, verify SOPs are followed, and provide continuous cycle-time analysis with unprecedented precision across various production environments.<h3 dir="ltr">Custom Detectors and Real-Time Monitoring</h3>Matroid's platform distinguishes itself through its no-code software to build and deploy detector systems, allowing manufacturing teams to develop specialized detection models through an intuitive point-and-click interface.&nbsp;These vision detectors can simultaneously evaluate multiple quality parameters in metal processing applications - from weld porosity and spatter to surface defects and assembly errors - while maintaining consistent accuracy across varying conditions.The platform's camera-agnostic system integrates seamlessly with existing infrastructure, supporting everything from standard industrial cameras to specialized equipment like borescopes, microscopes, and X-ray systems.&nbsp;Real-time alerts and analytics are delivered directly with production systems (PLCs, SCADA, MES, ERP) and mobile devices. This enables immediate response to the right people and systems at the right time to detect anomalies and maintain digital traceability throughout manufacturing.<h2 dir="ltr">How Matroid’s Computer Vision Powers Welding Applications</h2><iframe src="https://www.youtube.com/embed/20FpAGGAqmk?&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" style="width: 640px; height: 460px;"></iframe> <h3 dir="ltr">Detecting Defects in Metal Manufacturing</h3>Matroid's computer vision enables quality control oversight across the entire metal manufacturing process. The <a href="https://www.matroid.com/automated-visual-anomoly-detection/">system</a> identifies structural defects at the casting stage, like cracks, porosity, cold flow, and hot tears.&nbsp;Within the stamping processes, Matroid detectors are used to replace manual visual inspection by automating the inspection of stamped parts for tears and splits. &nbsp;This gives near real-time feedback to production engineers on how the presses are performing and gives insights into potential adjustments to prevent further defects.&nbsp;In the final inspection, the platform performs comprehensive checks to ensure consistently high quality, effectively reducing returns and warranty claims.<h3 dir="ltr">Computer Vision in Welding</h3>Matroid's technology monitors operations such as spot welding, MIG welding, and continuous seam welding through real-time monitoring and instant defect detection. The system evaluates multiple quality parameters simultaneously, identifying common issues like porosity, spatter, cold welds, edge welds, and burn-through, just as a weld engineering expert would.&nbsp;The platform's deep learning capabilities enable it to detect defects as a person could through visual media (images/video). The solutions are designed for rugged &amp; dynamic manufacturing environments.&nbsp;<h3 dir="ltr">Root Cause Analysis and Real-Time Adjustments</h3>Matroid's <a href="https://www.qualitymag.com/articles/98221-advances-in-computer-vision-offer-a-clearer-path-to-reliable-quality-control">solution</a> provides detailed defect classification and analysis through its integrated system. The system immediately alerts operators when issues are detected through direct integration with production systems, QMS, and mobile devices. &nbsp;Production teams can take corrective action when they have near real-time classification information and analytics; for example, when burn-through is detected on welds, the welding engineer can make more effective adjustments to robotic welding systems faster. &nbsp;This reduces rework and defect risks and improves production throughput.&nbsp;The platform operates 24/7, providing continuous monitoring and real-time insights into production operations. Through its historical data analysis capabilities, manufacturing teams can capture actionable insights and implement zero-defect initiatives.<h2 dir="ltr">Real-World Applications and Benefits&nbsp;</h2><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1731927333487-1731927333487.png" id="isPasted" class="fr-fic fr-dii"><h3 dir="ltr" id="isPasted">Traditional Methods vs. Computer Vision &amp; AI for Welding</h3>Traditional welding inspection methods, relying on manual or basic automated systems, provide only binary pass/fail results. These systems need help to meet modern manufacturing demands, where quality teams need detailed defect analysis and real-time process adjustments.&nbsp;Manual inspections introduce delays between defect occurrence and detection, while inspector fatigue and varying experience levels lead to inconsistent results. Computer vision &amp; AI solutions powered by deep learning technology transform this landscape by enabling real-time identification and classification of specific weld defects.&nbsp;Matroid's system automatically detects and categorizes issues such as porosity, burn-through, and spatter, immediately notifying weld engineers for rapid response. This immediate feedback loop allows manufacturing teams to quickly identify root causes and implement real-time process adjustments.The deployment of edge or on-premises installations ensures minimal latency for real-time monitoring while maintaining data security. This approach enables manufacturing teams to shift from reactive quality control to proactive process optimization.&nbsp;Through consistent quality assessment and automated defect classification, companies can reduce inspection time, minimize waste, and improve overall welding quality. The system's integration with existing production processes creates a seamless transition from traditional inspection methods to advanced computer vision &amp; AI solutions.<h2 dir="ltr">The Deployment of Matroid’s Solution</h2><h3 dir="ltr">Easy Integration and Deployment</h3>Matroid's computer vision &amp; AI platform integrates seamlessly with existing manufacturing infrastructure through multiple deployment options. The system can be implemented as downloadable software for on-premises installation or as a Cloud software service, allowing manufacturers to choose the deployment method that best suits their operational needs.&nbsp;This flexibility enables manufacturing teams to maintain data security while leveraging advanced detection capabilities. &nbsp;Matroid’s platform makes integration into various control systems, MES, QMS, ERP, and databases easy so that production can work with real-time data and has enhanced traceability of the products produced.&nbsp;<h3 dir="ltr">Compatibility with Multiple Camera Technologies</h3>The platform's camera-agnostic approach sets it apart in the manufacturing sector. Matroid works with existing and new vision systems, supporting any image-or-video-based camera regardless of resolution (VGA - giga-pixel) or spectra (visible, infrared, X-ray, etc.).This allows manufacturers to reuse their existing machine vision cameras and use the right imaging technology for the application at hand while maintaining the ability to detect visual defects. The system connects effortlessly with industrial cameras, supporting continuous 24/7 monitoring and real-time insights into manufacturing processes.<h2 dir="ltr">Future of Welding with Computer Vision &amp; AI</h2>Integrating computer vision technology in welding operations represents a significant leap forward in manufacturing quality control. Matroid's platform transforms traditional inspection methods by enabling real-time defect detection and detailed classification, which enables immediate process adjustments.&nbsp;By combining deep learning capabilities with enterprise-grade deployment options, manufacturers can achieve consistent quality while reducing operational costs. The platform's ability to empower domain experts in manufacturing, work with existing infrastructure, and support multiple camera technologies makes it a practical solution for facilities of all sizes.<h2 dir="ltr">Ready to make your welding operations efficient and reliable with advanced computer vision?</h2>Discover how Matroid's platform can enhance your quality control processes and drive real-time improvements. Visit Matroid's <a href="https://www.matroid.com/">website</a> to explore their innovative solutions for metal manufacturing.<h2 dir="ltr">References</h2><ol><li dir="ltr"><a href="https://info.matroid.com/visual-inspection-computer-vision-software">https://info.matroid.com/visual-inspection-computer-vision-software</a></li><li dir="ltr"><a href="https://www.dayjob.com.au/metal-fabrication-challenges-with-sustainability/">https://www.dayjob.com.au/metal-fabrication-challenges-with-sustainability/</a></li><li dir="ltr"><a href="https://www.matroid.com/automated-visual-anomoly-detection/">https://www.matroid.com/automated-visual-anomoly-detection/</a></li><li dir="ltr"><a href="https://www.qualitymag.com/articles/98221-advances-in-computer-vision-offer-a-clearer-path-to-reliable-quality-control">https://www.qualitymag.com/articles/98221-advances-in-computer-vision-offer-a-clearer-path-to-reliable-quality-control</a></li><li dir="ltr"><a href="https://www.weboccult.com/blog/computer-vision-for-quality-control-in-manufacturing-a-comprehensive-guide">https://www.weboccult.com/blog/computer-vision-for-quality-control-in-manufacturing-a-comprehensive-guide</a></li><li dir="ltr"><a href="https://cloud.google.com/blog/products/ai-machine-learning/improve-manufacturing-quality-control-with-visual-inspection-ai">https://cloud.google.com/blog/products/ai-machine-learning/improve-manufacturing-quality-control-with-visual-inspection-ai</a></li><li dir="ltr"><a href="https://www.matroid.com/automated-visual-anomoly-detection/">https://www.matroid.com/automated-visual-anomoly-detection/</a></li><li dir="ltr"><a href="https://www.qualitymag.com/articles/98221-advances-in-computer-vision-offer-a-clearer-path-to-reliable-quality-control">https://www.qualitymag.com/articles/98221-advances-in-computer-vision-offer-a-clearer-path-to-reliable-quality-control</a></li></ol>

Discover how computer vision is solving quality challenges in metal manufacturing through enhanced defect detection, proactive process monitoring, and flexible integration solutions.

AI-Powered Welding: How Computer Vision Solutions Drive Precision and Efficiency in Metal Manufacturing

<h3 id="isPasted">Rooster Drone: Revolutionizing Search-and-Rescue and Tactical Operations</h3>In the ever-evolving field of unmanned aerial vehicles (UAVs), the Rooster drone by Robotican stands out as a groundbreaking solution for search-and-rescue, tactical, and security operations. Engineered to be an agile, resilient, and intelligent UAV, the Rooster drone’s core capabilities enable it to perform in high-risk environments, making it an invaluable asset for defense forces and emergency response teams worldwide. Let's dive into the technical specifications and capabilities that make this drone a true innovation.<h4>Flight In Complex Environments</h4>Built to operate in GPS-denied environments. This system is particularly advantageous in environments with compromised visibility or structural instability, such as collapsed buildings or booby-trapped spaces.The Rooster’s AI-driven algorithms enable it to dynamically adapt to changing environments, This capability allows the Rooster to minimize risks to personnel, providing crucial data in situations where human entry would be too dangerous.<h4>Durable Design and Resilience</h4>Designed with operational durability in mind, the Rooster drone’s rugged frame and flexible, shock-absorbent materials give it the resilience needed for both indoor and outdoor operations. Unlike conventional drones that may be vulnerable in confined spaces, the Rooster’s robust body structure and flexible propeller guards ensure it can recover quickly after minor collisions, allowing it to continue its mission uninterrupted.Additionally, the Rooster’s compact design enables it to enter narrow passageways and navigate tight spaces—a significant advantage in scenarios where traditional, larger drones would be hindered. <img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1731312571642-Rooster+for+article.jpg" style="width: 100%px;" class="fr-fic fr-dib"><h4>Real-Time Data Transmission and Advanced Sensing Capabilities</h4>Equipped with high-definition cameras, thermal imaging, and variable sensors the Rooster is capable of delivering real-time visual and thermal data to operators on the ground. This multi-sensory data provides a comprehensive view of the operational environment, enabling better decision-making under high-pressure situations. Those features are particularly useful for operations in dimly lit or smoke-filled rooms where visibility is otherwise compromised.<h4>Simplified User Interface and Intuitive Control</h4>Robotican has designed the Rooster with an intuitive user interface that simplifies control for operators with varied technical backgrounds. The system’s interface can be accessed on standard tablets or laptops, providing a real-time, high-definition feed and mission controls. Operators can quickly command the drone with straightforward commands or set it on an autonomous route using its way-point navigation system. <img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1731313531966-For+article.jpg" style="width: 100%px;" class="fr-fic fr-dib">For defense applications, this ease of use translates to faster deployment and reduced training time. The Rooster’s controls are designed to be highly responsive, making it a powerful tool in fast-paced tactical scenarios where every second counts.<h4>Compact and Ready for Rapid Deployment</h4>The Rooster drone’s portability and readiness make it an ideal solution for situations requiring rapid deployment. With a fast setup time, it can be operational within minutes, significantly reducing the time between arrival on the scene and the commencement of the mission.Weighing under a few kilograms, the Rooster is compact enough to be carried in a backpack and deployed by a single operator. This lightweight design, combined with its modularity, allows for easy transport and swift adjustments based on mission requirements.<h4>Applications Across Diverse Operational Needs</h4>The versatility of the Rooster drone allows it to support a wide range of operations. From search-and-rescue missions in disaster-stricken areas to surveillance tasks in military and police operations, its capabilities serve as a force multiplier for teams in the field. In recent military applications, the Rooster has demonstrated its unique ability to identify threats, such as detecting improvised explosive devices (IEDs) in potentially hostile environments, saving lives and improving operational outcomes.Its usage has also extended to various emergency services, with fire departments and other first responders deploying the Rooster in challenging situations where human lives are at risk. The drone’s ability to rapidly scan a building for survivors or detect hotspots in a fire scenario has proven invaluable, showcasing its adaptability and life-saving potential.<h4>Conclusion</h4>The Rooster drone by Robotican exemplifies the future of autonomous drones in high-stakes environments. Its combination of autonomous navigation, durability, advanced sensing, and rapid deployment capabilities equips it to handle the complexities of modern tactical, rescue, and surveillance operations. Whether deployed by defense forces or emergency responders, the Rooster is setting new standards for UAV performance, reliability, and mission success.As the demand for resilient, autonomous solutions grows, the Rooster drone is poised to become an essential asset in the operational toolkit of those on the frontlines, pushing the boundaries of what is possible with unmanned aerial technology.

A Hybrid Robotic Drone Engineered for Precision and Resilience in High-Stakes Tactical and Search-and-Rescue Operation

Rooster Drone: Revolutionizing Search-and-Rescue and Tactical Operations

The ocean is a hostile environment for humans, so much so that some would consider it even more hostile than space. Like space, the deep ocean is extremely cold and completely dark; but unlike space's absence of pressure, the deep ocean has extreme pressure. Deep ocean explorers can only reach these depths using either a submarine or a submersible.Submarines, like those from the US Navy, are ships that can navigate long distances underwater without having to surface. They can navigate the world’s oceans with precision and at speeds upwards of 25 knots (46km/h). Their highly engineered hulls are constructed of layers of steel, titanium, and other metal alloys, capable of withstanding the extreme pressure of the ocean’s depths. These submarines are also highly compartmentalized to help mitigate any potential hull breach.Submarines are sleek, self-contained ships with mission-critical redundant safety systems, pinpoint accuracy navigation capabilities, and powerful propulsion systems capable of circumnavigating the globe without refueling. For the most part, a submarine’s crew can move about freely and carry on with normal life, close quarters notwithstanding. &nbsp;A submersible’s mission typically revolves around exploration or excursions and, like submarines, they can descend into the ocean’s depths. But that is about where the similarities end. Compared to submarines, submersibles are almost the complete opposite. In general, submersibles tend to be small and clunky and lack sleekness. Submersibles are usually only capable of carrying a limited crew of 1–3 individuals who serve as operators and researchers. Also, their hulls are generally engineered as a unibody structure, not necessarily made of steel, and typically feature port holes or large plexiglass globes as windows for the crew—something submarines lack. Additionally, submersibles have limited propulsion systems powered by batteries that propel the vessel at low speeds.Because of their size, submersibles lack water-tight compartmentalization, meaning a hull breach can have disastrous consequences. A submersible’s dive missions are typically short—in the order of hours— and typically rely on a surface mother ship for support, so many of their onboard safety, navigation, and life support systems do not typically employ redundancy.<h2 dir="ltr">Extreme Water Pressure</h2>To get an idea of the immense pressure at the depth of the Titanic wreckage, let’s consider that the air pressure that surrounds us (referred to as one atmosphere) is 1013mbar or 14.7 pounds per square inch (PSI). We humans do not feel this pressure because the fluids in our bodies are pushing out, helping to equalize this pressure. But this all changes the deeper you go under the ocean; the pressure of the water pushing down on you increases approximately one atmosphere for every 10 meters of water depth. For example, at the depth where the Titanic wreckage lies, the pressure is approximately 385 atmospheres, or 385 times greater than the pressure at sea level. The fact that numerous sea-dwelling creatures face no difficulty coping with this high pressure, frigid 2.5°C water, and total darkness is simply nature at work.<h2 dir="ltr">Featured Products</h2>This week's New Tech Tuesday introduces two sensing products. One enables wireless monitoring of water leaks, while the other allows monitoring of humidity and temperature in critical electronics. With precise measurements and ultra-low power consumption, these dependable products can help keep water and moisture under control in your future engineering projects.The <a href="https://www.mouser.com/new/multitech/multitech-wireless-water-leak-sensors/">LoRaWAN®&nbsp;Wireless Leak Detection Sensors from MultiTech</a> offer a highly effective solution to mitigate water damage in a range of environments. These sensors are available as 1-meter rope sensors for indoor, outdoor, or industrial use. In puck form, these sensors can promptly send alerts over a wireless network upon detection of water to prevent catastrophic damage. Leveraging LoRaWAN technology, these remote detectors can be strategically placed near water heaters, standing tanks, toilets, septic systems, floor drains, and other potential leakage points to preempt costly water damage.The sensors are built for IoT applications, engineered for long-range transmission, and offer an extended battery life of 5–10 years at an affordable cost. They feature an open architecture design, allowing for flexible integration with other systems. A notable advantage is the optional web-based console for real-time provisioning, monitoring, and configuration of sensors in the field.These sensors also have applications in monitoring plumbing and reservoir leaks, burst water pipe surveillance, data center leak, and water intrusion monitoring. These capabilities make them ideal for complex facilities, with custom designs available to meet specific needs.Integrating these sensors on the same network allows monitoring from even the most remote locations. The sensors also feature long-range, excellent wireless penetration through structures, enclosure tamper detection, automatic error reporting, and over-the-air configuration. All these features and benefits reinforce the role of MultiTech's wireless leak detection sensors in effectively managing and reducing liquid leak-related risks.The <a href="https://www.mouser.com/new/sensirion/sensirion-sht4x-digital-sensor/">Sensirion 4th Generation SHT4x Digital Humidity and Temperature Sensors</a> offer a range of benefits for climatic management of critical electronics. These sensors feature high accuracy and ultra-low power consumption, making them ideal for mobile and battery-driven applications. With their extended supply voltage range and improved accuracy specifications, they are well-suited for climate control, anti-fogging, and relative humidity/temperature (RH/T) condition monitoring. The sensors are compact and housed in a robust DFN package, allowing for easy integration into challenging designs while meeting the highest reliability standards. Additionally, the <a href="https://www.mouser.com/new/sensirion/sensirion-senseval-sht4xv1-eval-board/">SENSEVAL-SHT4xV1 Evaluation Board</a> simplifies sensor evaluation and prototyping, with compatibility for various STMicroelectronics boards. The I²C interface provides flexibility, and the internal heater enables sensor operation in demanding environments. These sensors are packaged in a surface mount technology (SMT) compatible format, making them suitable for high-volume applications. Sensirion's proven sensor solutions are widely used across different applications and markets.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1730708637663-sensor.webp" style="width: 832px;" class="fr-fic fr-dib">MultiTech LoRaWAN Wireless IoT Sensors<h3 id="isPasted"> </h3> <h2>Conclusion</h2>The ocean’s immense water pressure at great depths poses a significant threat to humans and machines alike.In the realm of technology, this week’s featured products offer innovative solutions for detecting water leaks and monitoring moisture and temperature. The potential damage caused by water leaks and moisture to electronics cannot be overlooked. The featured products from MultiTech and Sensirion play a crucial role in preventing such damage by providing early detection and monitoring capabilities. Implementing these innovative sensing technologies enables engineers to effectively mitigate the risks associated with water leakages or moisture buildup, ensuring the longevity and optimal performance of critical electronic systems.

This article introduces two sensing products. One enables wireless monitoring of water leaks, while the other allows monitoring of humidity and temperature in critical electronics. 

Perils of the Deep: Mission Critical Sensing Technology Solutions

In the classic cartoon “The Jetsons,” Rosie the robotic maid seamlessly switches from vacuuming the house to cooking dinner to taking out the trash. But in real life, training a general-purpose robot remains a major challenge.Typically, engineers collect data that are specific to a certain robot and task, which they use to train the robot in a controlled environment. However, gathering these data is costly and time-consuming, and the robot will likely struggle to adapt to environments or tasks it hasn’t seen before.To train better general-purpose robots, MIT researchers developed a versatile technique that combines a huge amount of heterogeneous&nbsp;data from many of sources into one system that can teach any robot a wide range of tasks.Their method involves aligning data from varied domains, like simulations and real robots, and multiple modalities, including vision sensors and robotic arm position encoders, into a shared “language” that a generative AI model can process.By combining such an enormous amount of data, this approach can be used to train a robot to perform a variety of tasks without the need to start training it from scratch each time.This method could be faster and less expensive than traditional techniques because it requires far fewer task-specific data. In addition, it outperformed training from scratch by more than 20 percent in simulation and real-world experiments.“In robotics, people often claim that we don’t have enough training data. But in my view, another big problem is that the data come from so many different domains, modalities, and robot hardware. Our work shows how you’d be able to train a robot with all of them put together,” says Lirui Wang, an electrical engineering and computer science (EECS) graduate student and lead author of a <a href="https://arxiv.org/pdf/2409.20537" target="_blank">paper on this technique</a>.Wang’s co-authors include fellow EECS graduate student Jialiang Zhao; Xinlei Chen, a research scientist at Meta; and senior author Kaiming He, an associate professor in EECS and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). The research will be presented&nbsp;at the Conference on Neural Information Processing Systems.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1730247515325-MIT-HeteroGenRobot-02-press.jpg" style="width: 100%px;" class="fr-fic fr-dib">A figure shows how the new technique aligns data from varied domains, like simulation and real robots, and multiple modalities, including vision sensors and robotic arm position encoders, into a shared “language” that a generative AI model can process. Image: Courtesy of the researchers<h2>Inspired by LLMs</h2>A robotic “policy” takes in sensor observations, like camera images or proprioceptive measurements that track the speed and position a robotic arm, and then tells a robot how and where to move.Policies are typically trained using imitation learning, meaning a human demonstrates actions or teleoperates a robot to generate data, which are fed into an AI model that learns the policy. Because this method uses a small amount of task-specific data, robots often fail when their environment or task changes.To develop a better approach, Wang and his collaborators drew inspiration from large language models like GPT-4.These models are pretrained using an enormous amount of diverse language data and then fine-tuned by feeding them a small amount of task-specific data. Pretraining on so much data helps the models adapt to perform well on a variety of tasks.“In the language domain, the data are all just sentences. In robotics, given all the heterogeneity in the data, if you want to pretrain in a similar manner, we need a different architecture,” he says.Robotic data take many forms, from camera images to language instructions to depth maps. At the same time, each robot is mechanically unique, with a different number and orientation of arms, grippers, and sensors. Plus, the environments where data are collected vary widely.The MIT researchers developed a new architecture called Heterogeneous Pretrained Transformers (HPT) that unifies data from these varied modalities and domains.They put a machine-learning model known as a transformer into the middle of their architecture, which processes vision and proprioception inputs. A transformer is the same type of model that forms the backbone of large language models.The researchers align data from vision and proprioception into the same type of input, called a token, which the transformer can process. Each input is represented with the same fixed number of tokens.Then the transformer maps all inputs into one shared space, growing into a huge, pretrained model as it processes and learns from more data. The larger the transformer becomes, the better it will perform.A user only needs to feed HPT a small amount of data on their robot’s design, setup, and the task they want it to perform. Then HPT transfers the knowledge the transformer grained during pretraining to learn the new task.<h2>Enabling dexterous motions</h2>One of the biggest challenges of developing HPT was building the massive dataset to pretrain the transformer, which included 52 datasets with more than 200,000 robot trajectories in four categories, including human demo videos and simulation.The researchers also needed to develop an efficient way to turn raw proprioception signals from an array of sensors into data the transformer could handle.“Proprioception is key to enable a lot of dexterous motions. Because the number of tokens is in our architecture always the same, we place the same importance on proprioception and vision,” Wang explains.When they tested HPT, it improved robot performance by more than 20 percent on simulation and real-world tasks, compared with training from scratch each time. Even when the task was very different from the pretraining data, HPT still improved performance.“This paper provides a novel approach to training a single policy across multiple robot embodiments. This enables training across diverse datasets, enabling robot learning methods to significantly scale up the size of datasets that they can train on.&nbsp;It also allows the model to quickly adapt to new&nbsp;robot embodiments, which is important as new robot designs are continuously being produced,” says David Held, associate professor at the Carnegie Mellon University Robotics Institute, who was not involved with this work.In the future, the researchers want to study how data diversity could boost the performance of HPT. They also want to enhance HPT so it can process unlabeled data like GPT-4 and other large language models.“Our dream is to have a universal robot brain that you could download and use for your robot without any training at all. While we are just in the early stages, we are going to keep pushing hard and hope scaling leads to a breakthrough in robotic policies, like it did with large language models,” he says.

Researchers filmed multiple instances of a robotic arm feeding co-author Jialiang Zhao's adorable dog, Momo. The videos were included in datasets to train the robot. Image: Courtesy of the researchers

Inspired by large language models, researchers develop a training technique that pools diverse data to teach robots new skills.

A faster, better way to train general-purpose robots

This comprehensive article dives deep into the world of robotics, exploring the history, types, engineering components, applications, and future trends of robots, offering readers an in-depth understanding of how these remarkable machines work and shape our lives.

What is Robotics? A Comprehensive Guide to its Engineering Principles and Applications

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Introduction

Edge AI Market Analysis and Trends

Healthcare and Medical Applications

Industrial IoT and Manufacturing

Smart Cities and Urban Infrastructure

Retail and Customer Experience

Energy Efficiency and Sustainability

Agriculture and Food Production 

Automotive and Transportation

Generative AI at the Edge

Edge AI Challenges and Real-World Mitigations

The Future of Edge AI

Exploring the Dynamic World of Edge AI Applications Across Industries


2024 State of Edge AI Report

robotics

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Delve into the basics of absolute encoders, its applications, advancements in technologies, calibration techniques, and the advantages over encoder types.


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Arduino micro-controller on the left, Raspberry Pi in the middle and C.H.I.P. single-board computer on the right

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Robotics

2024 State of Edge AI Report

Introduction

1. Edge AI Market Analysis and Trends

2. Healthcare and Medical Applications

3. Industrial IoT and Manufacturing

4. Smart Cities and Urban Infrastructure

5. Retail and Customer Experience

6. Energy Efficiency and Sustainability

7. Agriculture and Food Production

8. Automotive and Transportation

9. Generative AI at the Edge

10. Edge AI Challenges and Real-World Mitigations

11. The Future of Edge AI

What is Robotics? A Comprehensive Guide to its Engineering Principles and Applications

Profiles