Residual Compliant Policy: Learning Compliant Policies from Few Demonstrations
Overview
A new framework learns compliant policies from few demonstrations, combining teleoperation, simulation, and residual reinforcement learning. Evaluated on flipping and pushing tasks with a Franka FR3, it achieves 35/40 success in out-of-distribution trials, outperforming baselines.
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Reported quantity · demonstrations: 7 other · Basis not reported
“of 7 demonstrations across tasks. Our method is successful on all 40 out-of-distribution trials, compared to 33/40 and 24/40 for two baselines, with our base policy alone reaching 35/40.”
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Reported quantity · out_of_distribution_trials: 40 other · Basis not reported
“of 7 demonstrations across tasks. Our method is successful on all 40 out-of-distribution trials, compared to 33/40 and 24/40 for two baselines, with our base policy alone reaching 35/40.”
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Reported quantity · tasks: 3 other · Basis not reported
“trajectories efficiently but is not physics-consistent during contact, with simulation-based trajectory optimization, which enforces physical plausibility but offers no manual guidance. Finally, a residual reinforcement learning policy refines the interaction forces by adjusting the reference pose of the underlying admittance controller. We evaluate on three flipping and pushing tasks on a Franka FR3 based on a total”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsResidual Compliant Policy: Learning Compliant Policies from Few Demonstrations
This research presents a framework for learning compliant policies from few demonstrations, combining teleoperation, simulation-based trajectory optimization, and residual reinforcement learning to improve contact-rich manipulation tasks. The method is evaluated on three flipping and pushing tasks using a Franka FR3 robot, achieving success in 35 out of 40 out-of-distribution trials, outperforming two baselines.
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