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This research addresses a critical bottleneck in humanoid robotics by introducing HOI-Retarget, a method that adapts human-object interaction data to robot kinematics through contact-centric optimization. By enabling the transfer of demonstrations across varying object sizes and reconstructing contacts from monocular video, the work significantly expands the scale and diversity of available motion datasets for learning from demonstration paradigms. The release of both code and retargeted motions provides a valuable resource for advancing whole-body manipulation skills in next-generation robotic systems.
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HOI-Retarget: Contact-Centric Retargeting for Human-Object Interaction

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2609.34674v1 Announce Type: new Abstract: Learning from demonstration (LfD) has enabled humanoid robots to acquire diverse whole-body skills, but extending this paradigm to human-object interaction (HOI) is limited by the availability of robot-compatible interaction references. We present HOI-Retarget, a contact-centric retargeting method that transfers HOI onto a humanoid robot for large-scale motion-data generation. Its windowed trajectory optimization uses every labeled contact as a target in the object frame, balancing body tracking, foot support and smoothness under the robot's kinematic limits. The method can augment a single demonstration across object sizes, absorb contacts reconstructed from monocular video, and extend to several robots manipulating one object. We publicly release the code and the retargeted motion dataset.

Abstract:Learning from demonstration (LfD) has enabled humanoid robots to acquire diverse whole-body skills, but extending this paradigm to human-object interaction (HOI) is limited by the availability of robot-compatible interaction references. We present HOI-Retarget, a contact-centric retargeting method that transfers HOI onto a humanoid robot for large-scale motion-data generation. Its windowed trajectory optimization uses every labeled contact as a target in the object frame, balancing body tracking, foot support and smoothness under the robot's kinematic limits. The method can augment a single demonstration across object sizes, absorb contacts reconstructed from monocular video, and extend to several robots manipulating one object. We publicly release the code and the retargeted motion dataset.

8 pages, 6 figures. Submitted to ICRA. Website: this http URL

Robotics (cs.RO)

arXiv:2609.34674 [cs.RO]

(or arXiv:2609.34674v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.34674 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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