Imitation Learning
High-quality sense-of-touch data is becoming a fundamental requirement for robotics imitation learning, especially as robots must accurately reproduce human skills. Modern robot learning from demonstration systems increasingly depend on detailed sense-of-touch information to generate reliable behavioral cloning in robotics and robust robot skill learning. As human-robot learning advances, high performance haptic feedback such as force feedback device enables more stable robot policy learning and improves the consistency of demonstration-based robotics in both real-world and simulated-world environments.
“Our research in robotics is aimed at amplifying people rather than replacing them,” said Gill Pratt, Chief Scientist for Toyota Motor Corporation. “This new teaching technique is both very efficient and produces very high performing behaviors, enabling robots to much more effectively amplify people in many ways. Previous state-of-the-art techniques to teach robots new behaviors were slow, inconsistent, inefficient, and often limited to narrowly defined tasks performed in highly constrained environments.”
Source : https://pressroom.toyota.com/toyota-research-institute-unveils-breakthrough-in-teaching-robots-new-behaviors/
Advances in inverse reinforcement learning in robotics, generative models for robot imitation, and diffusion models for robot control are transforming how robots acquire new skills. Emerging foundation models for robotics, offline imitation learning, and policy cloning for robotics enable greater autonomy, while techniques such as kinesthetic teaching for robots, high-quality demonstration datasets for robotics, and robust robot motion imitation push real-world performance forward. These innovations are increasingly critical for industrial robotics imitation learning, surgical robotics, service robots, robotic assembly, human-robot collaboration across sectors.
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Illustration of the “Plug & Play” bundle VIRTUOSE + TREX to enable real time data collection.
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