TDC: Sim-to-Real Transferable Directional Compliance for Contact-Rich Manipulation

arXiv:2602.14174v2 Announce Type: replace Abstract: Contact-rich manipulation requires robots to regulate both motion and interaction forces, yet achieving adaptive compliance remains a fundamental challenge. Learning from real-world data is costly and risky, while simulation-based approaches struggle with the sim-to-real gap in contact dynamics; existing sim-to-real methods either require real-world adaptation or sacrifice adaptive compliance by relying on isotropic compliant controllers. Our key insight is that force regulation decomposes into a time-varying but simulation-transferable directional component and a dynamics-sensitive but manually tunable magnitude component. We instantiate this directional component as two policy outputs, a task frame and a control mode vector, predicted by a visuomotor policy adapted from a pre-trained VLA model and trained via imitation learning on automatically generated simulation demonstrations. At deployment, an admittance controller integrates these predictions with human-specified stiffness and target wrench values to realize adaptive compliance. Our approach achieves adaptive compliance using only simulation data and can benefit from large-scale VLA pre-training. Extensive real-world experiments on four contact-rich tasks, microwave opening, peg-in-hole insertion, whiteboard wiping, and door opening, demonstrate strong task success rates and robustness to external disturbances. Project page: https://yifei-y.github.io/project-pages/TDC/.

Abstract:Contact-rich manipulation requires robots to regulate both motion and interaction forces, yet achieving adaptive compliance remains a fundamental challenge. Learning from real-world data is costly and risky, while simulation-based approaches struggle with the sim-to-real gap in contact dynamics; existing sim-to-real methods either require real-world adaptation or sacrifice adaptive compliance by relying on isotropic compliant controllers. Our key insight is that force regulation decomposes into a time-varying but simulation-transferable directional component and a dynamics-sensitive but manually tunable magnitude component. We instantiate this directional component as two policy outputs, a task frame and a control mode vector, predicted by a visuomotor policy adapted from a pre-trained VLA model and trained via imitation learning on automatically generated simulation demonstrations. At deployment, an admittance controller integrates these predictions with human-specified stiffness and target wrench values to realize adaptive compliance. Our approach achieves adaptive compliance using only simulation data and can benefit from large-scale VLA pre-training. Extensive real-world experiments on four contact-rich tasks, microwave opening, peg-in-hole insertion, whiteboard wiping, and door opening, demonstrate strong task success rates and robustness to external disturbances. Project page: this https URL.

Robotics (cs.RO)

arXiv:2602.14174 [cs.RO]

(or arXiv:2602.14174v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2602.14174 Focus to learn more arXiv-issued DOI via DataCite

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TDC: Sim-to-Real Transferable Directional Compliance for Contact-Rich Manipulation

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