MATCH trains hybrid position-force RL policies that achieve up to 10% higher success rates and 5x fewer breaks than pose-only policies in fragile peg-in-hole tasks under localization uncertainty, with strong sim-to-real results.
Variable impedance control in end-effector space: An action space for reinforcement learning in contact-rich tasks,
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Framework generates force-informed sim data from one demo to train compliant visuomotor flow matching policies, showing reliable contact on real-robot block flipping and bi-manual tasks.
citing papers explorer
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Learning Hybrid-Control Policies for High-Precision In-Contact Manipulation Under Uncertainty
MATCH trains hybrid position-force RL policies that achieve up to 10% higher success rates and 5x fewer breaks than pose-only policies in fragile peg-in-hole tasks under localization uncertainty, with strong sim-to-real results.
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Flow with the Force Field: Learning 3D Compliant Flow Matching Policies from Force and Demonstration-Guided Simulation Data
Framework generates force-informed sim data from one demo to train compliant visuomotor flow matching policies, showing reliable contact on real-robot block flipping and bi-manual tasks.