KAI: A Kinematic-Aware Interface for Data-Efficient Articulated Object Manipulation

Abstract:Articulated object manipulation requires an understanding of kinematic structure that is difficult and costly to learn from robot demonstrations alone. We introduce the Kinematic-Aware Articulation Interface (KAI), a structured intermediate representation that captures the kinematic structure of articulated objects. By embedding interpretable geometric and kinematic priors into policy learning, KAI provides a strong inductive bias aligned with the underlying structure of articulated motion. This design effectively improves sample efficiency, with gains particularly pronounced in low-data regimes: across six simulation tasks, our method achieves an average success rate of 82.9%, matching or surpassing baseline performance while using only half the demonstration data. Our method also exhibits robust generalization to unseen backgrounds and visual distractors, transferring from a single clean training environment to cluttered real-world scenes. KAI's action-agnostic design further enables co-training with human interaction videos to enhance real-world robustness: under diverse visual distractions, our method with video co-training achieves over 70% average success rate.

Robotics (cs.RO)

arXiv:2607.24493 [cs.RO]

(or arXiv:2607.24493v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2607.24493 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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