aiqbench analysis
The paper proposes a pipeline that turns simulation-generated videos into executable dexterous robot demonstrations, using foundation-model features for tracking and residual reinforcement learning to correct imperfect video-derived motion. The reported gains are modest: competitive results on clean ARCTIC data and better robustness on generated-video references. Readers should note that the real-robot evidence is limited to open-loop trajectory transfer, so closed-loop reliability remains unproven.
Gen2Dex: Learning Dexterous Manipulation from Simulation-Initialized Generated Video Demonstrations
arXiv:2610.24392v1 Announce Type: new Abstract: Human video demonstrations provide rich manipulation strategies for dexterous manipulation learning. We study a setting in which a fully specified simulated scene is used to generate videos that specify the demonstrated manipulation behavior. This setup provides access to scene information such as object geometry, articulation, and camera parameters, while allowing direct control over task and scene configuration. However, generated videos may contain geometric distortions, inaccurate contacts, and other physical inconsistencies, making the recovered motion difficult to execute directly on a robot. We present an end-to-end framework for converting such simulation-grounded videos into physically executable dexterous robot demonstrations. Our perception pipeline leverages the known scene through model-based object tracking with dense foundation-model features and depth-based hand-object alignment. The recovered human motion is then retargeted and physically grounded using residual reinforcement learning, where a backward curriculum and a cache of physically valid reset states enable the policy to correct imperfect video-derived references while preserving the demonstrated manipulation strategy. On clean ARCTIC demonstrations, our method achieves competitive performance with prior dexterous retargeting approaches and shows greater robustness when applied to references extracted from generated videos. We further demonstrate diverse rigid and articulated manipulation tasks in simulation and successful open-loop transfer of the optimized trajectories to a real dexterous robot.
Abstract:Human video demonstrations provide rich manipulation strategies for dexterous manipulation learning. We study a setting in which a fully specified simulated scene is used to generate videos that specify the demonstrated manipulation behavior. This setup provides access to scene information such as object geometry, articulation, and camera parameters, while allowing direct control over task and scene configuration. However, generated videos may contain geometric distortions, inaccurate contacts, and other physical inconsistencies, making the recovered motion difficult to execute directly on a robot. We present an end-to-end framework for converting such simulation-grounded videos into physically executable dexterous robot demonstrations. Our perception pipeline leverages the known scene through model-based object tracking with dense foundation-model features and depth-based hand-object alignment. The recovered human motion is then retargeted and physically grounded using residual reinforcement learning, where a backward curriculum and a cache of physically valid reset states enable the policy to correct imperfect video-derived references while preserving the demonstrated manipulation strategy. On clean ARCTIC demonstrations, our method achieves competitive performance with prior dexterous retargeting approaches and shows greater robustness when applied to references extracted from generated videos. We further demonstrate diverse rigid and articulated manipulation tasks in simulation and successful open-loop transfer of the optimized trajectories to a real dexterous robot.
Robotics (cs.RO)
arXiv:2610.24392 [cs.RO]
(or arXiv:2610.24392v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.24392 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 297 words