Study: Inertia mismatch affects robot policy learning
What happened
Younghyo Park, Antonia Bronars, and Pulkit Agrawal published a paper investigating how inertia mismatch between a robot and an object affects robot policy learning through controlled simulation experiments that varied the masses of both. The results show that reinforcement learning succeeds across a wide range of mass combinations; behavior cloning, in contrast, learns more reliable policies when a lighter robot manipulates heavier objects. The study also found that heavier robots require more demonstration data to achieve the same performance as lighter robots.
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- arXiv cs.ROStudy: How inertia mismatch affects robot policy learning when robots are heavier than objects
This paper investigates how inertia mismatch between a robotic arm and the objects it manipulates affects robot policy learning. Through controlled simulation experiments that vary robot and object masses, it finds that reinforcement learning succeeds across a broad range of mass combinations, while behavior cloning learns more reliable policies when a lighter robot manipulates a heavier object.
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