mlsanityData to 7 Oct 2026

HOI-Retarget: Contact-Centric Retargeting for Human-Object Interaction

arXiv 2609.34674 ↗First posted 28 Sep 2026cs.ROcited by 0

On the map: Offline RL and imitation learning from data → Robot manipulation imitation learning also fits: Real-world robot offline RL

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data. First posted on arXiv 28 Sep 2026.

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