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This research introduces a novel method for warping spatio-temporal trajectories in contact-rich dexterous manipulation, enabling the repurposing of human motion data across varied robotic configurations. By leveraging contact distributions to compute high-dimensional hand trajectories, the approach facilitates generalization from simple object-centric specifications to complex physical interactions. This advancement directly supports the development of more adaptable and robust AI-driven robotic systems capable of handling intricate tasks.
Kinematic Nonlinear Spatio-Temporal Trajectory Warping for Contact-Rich Dexterous Manipulation Demonstrations
arXiv:2609.36676v1 Announce Type: new Abstract: We present a straightforward but effective method for repurposing existing contact-rich dexterous manipulation demonstrations. Starting from inputs of hand and object trajectories, our method outputs high-quality nonlinear trajectory warps that account for intermediate waypoints, environmental barriers, temporal shifts, and varied start/end configurations. Foundational to our method is the utilization of contact distributions, which we show allows us to reliably compute complex and high-dimensional dexterous hand trajectories following a simple object-centric warp specification pipeline. We evaluate our method across 12 variations sourced from 4 demonstrations in a publicly available dataset of human hand motion data, perform baseline comparisons, and demonstrate generalization of our approach to different manipulators. Results and code will be made available on publication.
Abstract:We present a straightforward but effective method for repurposing existing contact-rich dexterous manipulation demonstrations. Starting from inputs of hand and object trajectories, our method outputs high-quality nonlinear trajectory warps that account for intermediate waypoints, environmental barriers, temporal shifts, and varied start/end configurations. Foundational to our method is the utilization of contact distributions, which we show allows us to reliably compute complex and high-dimensional dexterous hand trajectories following a simple object-centric warp specification pipeline. We evaluate our method across 12 variations sourced from 4 demonstrations in a publicly available dataset of human hand motion data, perform baseline comparisons, and demonstrate generalization of our approach to different manipulators. Results and code will be made available on publication.
Robotics (cs.RO)
arXiv:2609.36676 [cs.RO]
(or arXiv:2609.36676v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.36676 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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