AI-enabled robots and the importance of force sensing

Machine Learning (ML)

Machine learning allows AI-enabled robots to recognize patterns, make predictions, and continuously improve performance through data-driven learning. For example, AI robots can identify an object from visual information like cameras.

Deep Learning

A subset of machine learning, deep learning uses neural networks to enable AI-powered robots to process vast amounts of data and learn complex tasks, such as image recognition and natural language processing.

Reinforcement Learning (RL)

Reinforcement learning is a trial-and-error-based learning approach, where AI-enabled robots learn optimal actions through rewards and penalties in dynamic environments. Usually, these are physical actions like walking or running and, to a lesser extent manipulating objects. 

RL has shown impressive results in solving locomotion problems for walking robots. A key factor contributing to RL's success in robotics is that the trial-and-error process can be run multiple times in a simulated environment without the need to test on the actual robot. 

However, RL faces limitations in contact-rich manipulation tasks, such as cleaning a dish or folding a cloth. This is mainly due to the fact that simulated physical contact models are unable to fully capture real-world dynamics.

Imitation Learning 

Imitation learning is a technique in machine learning where the AI learns to perform tasks by observing and mimicking expert behavior, rather than being explicitly programmed. It is commonly used in robotics, where the goal is to teach robots to replicate human actions by watching demonstrations. 

This approach enables the robot to acquire complex skills more naturally and intuitively, as it learns directly from examples rather than trial-and-error or predefined rules. 

Imitation learning bridges the gap between human expertise and autonomous machine behavior, making it useful for applications where direct programming is difficult or impractical, such as in dynamic, and unstructured real-world environments.

In practice, the data required involve human demonstrations. A person on one end, teleoperates the robot through a “leader” device to command directly a “follower” robot. The “leader” makes sure that the “follower” achieves the tasks. The data are recorded and fed to the AI for training. 

This is done multiple times until all, or a sufficient amount of “episodes” are recorded. These episodes include data from the robot sensing and actuating system.

Some other techniques involve demonstrations from videos where the behavior of humans is recorded. Force sensing in this case is impossible since the video includes only positional information.

Natural Language Processing (NLP) 

Natural language processing enables robots to understand and respond to human language, enhancing communication and human-robot collaboration. It is usually required to identify and break down the commands given through human language so it can subsequently be fed into the robot’s decision-making unit, in which it can identify if the task is physically possible.

Now that we are familiar with the key AI technologies, let's move on to how these technologies aid in model training in robotics.

Model Training in Robotics

Model training in robotics refers to the process by which robots use data from their sensors to improve their behaviors, predict future outcomes, and make autonomous decisions. 

Through AI technologies and algorithms described earlier, robots can develop models of their environment and their own physical capabilities, which enable them to perform tasks without constant human intervention. 

For instance, a robot that learns how to navigate through a cluttered space can apply that knowledge to different environments with minimal reprogramming.

Humanoids, which are designed to resemble human forms, benefit significantly from these AI methods because they must operate in human-centric environments. Through continuous learning, they can adapt to new tasks such as carrying objects, interacting with people, or responding to dynamic changes in the environment. 

Tesla’s humanoid robots are an excellent example, utilizing end-to-end AI to perform tasks based on real-time data rather than pre-set rules. These robots, initially teleoperated by humans for data collection, learn and refine their models through repeated demonstrations, improving their performance over time.

Tesla Optimus Gen 2 robotic humanoid