aiqbench analysis
This research introduces KAI, an intermediate representation that embeds kinematic priors into robot policy learning to significantly boost sample efficiency for manipulating articulated objects like doors or drawers. By leveraging geometric structures and co-training with human videos, the method achieves robust performance in low-data regimes while generalizing effectively from simulation to cluttered real-world environments. This advancement addresses a core challenge in robotics: reducing the massive data requirements needed to teach robots complex physical interactions without explicit kinematic modeling.
KAI: A Kinematic-Aware Interface for Data-Efficient Articulated Object Manipulation
arXiv:2607.24493v1 Announce Type: new 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.
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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Full text under the publisher's open licence · 246 words
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