When Robots Outweigh Objects: How Inertia Mismatch Shapes Robot Policy Learning
Overview
This research investigates how inertia mismatch between heavy robot arms and light objects affects policy learning. Through controlled simulation experiments varying robot and object masses, the authors find 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. They also demonstrate that increasing data narrows but does not close the performance gap between methods, and that behavior cloning degrades as simulated robot arm inertia increases via gearing even with constant weight. The work explores practical ways to lower contact inertia by selecting lower-inertia workspaces or steering demonstrators toward lower-inertia task solutions, showing that such demonstrations are imitated more reliably.
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“that even extreme inertia mismatch can be overcome with sufficient data. However, it remains unclear whether high inertia imposes a hidden cost on learning, potentially worsening scaling with data or lowering the performance ceiling of modern robot learning methods. We investigate this question through controlled simulation experiments that vary robot and object masses. We find that reinforcement learning succeeds ac”
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- arXiv Robotics — research abstractsSignalWhen Robots Outweigh Objects: How Inertia Mismatch Shapes Robot Policy Learning
This research investigates how inertia mismatch between heavy robot arms and light objects affects policy learning. Through controlled simulation experiments varying robot and object masses, the authors find 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. They also demonstrate that increasing data narrows but does not close the performance gap between methods, and that behavior cloning degrades as simulated robot arm inertia increases via gearing even with constant weight. The work explores practical ways to lower contact inertia by selecting lower-inertia workspaces or steering demonstrators toward lower-inertia task solutions, showing that such demonstrations are imitated more reliably.
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