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KITE addresses the core challenge of generalizing robotic manipulation policies across different physical embodiments without requiring task-specific retraining. By decoupling interaction prediction from kinematic decoding, this method enables zero-shot transfer to structurally distinct robots using only their kinematic models and synthetic data. This advancement significantly improves the scalability and adaptability of embodied AI systems for diverse hardware configurations.
KITE: Decoupling Kinematics and Interaction for Zero-Shot Cross-Embodiment Manipulation
arXiv:2606.22113v2 Announce Type: replace Abstract: Generalizing manipulation policies across robot embodiments remains difficult because standard policies tie the interactions needed for a task to the actions of a particular robot. We study zero-shot cross-embodiment manipulation, where a policy trained on source embodiments must be deployed on a structurally different target embodiment without additional task demonstrations. We introduce Kinematic Interaction Transfer across Embodiments (KITE), which decouples manipulation into embodiment-agnostic interaction prediction and embodiment-specific kinematic decoding, connected through a learned representation of interaction intent based on contact patterns. An interaction policy is trained to predict the intent using demonstration data only collected on a source robot, and shared across morphologically different target robots. A kinematic decoder realizes these intents by learning from synthetic data generated from each embodiment's kinematic model. Through such decoupling, KITE can robustly transfer learned skills to a new embodiment given only its kinematic model, without recollecting any task-specific demonstration data. We evaluate KITE on four manipulation tasks in simulation and the real world spanning transfer between parallel grippers, dexterous hands, and composite embodiments. KITE successfully achieves zero-shot transfer to structurally different target embodiments, and consistently outperforms state-of-the-art baselines. Project website: https://kite-manip.github.io/.
Abstract:Generalizing manipulation policies across robot embodiments remains difficult because standard policies tie the interactions needed for a task to the actions of a particular robot. We study zero-shot cross-embodiment manipulation, where a policy trained on source embodiments must be deployed on a structurally different target embodiment without additional task demonstrations. We introduce Kinematic Interaction Transfer across Embodiments (KITE), which decouples manipulation into embodiment-agnostic interaction prediction and embodiment-specific kinematic decoding, connected through a learned representation of interaction intent based on contact patterns. An interaction policy is trained to predict the intent using demonstration data only collected on a source robot, and shared across morphologically different target robots. A kinematic decoder realizes these intents by learning from synthetic data generated from each embodiment's kinematic model. Through such decoupling, KITE can robustly transfer learned skills to a new embodiment given only its kinematic model, without recollecting any task-specific demonstration data. We evaluate KITE on four manipulation tasks in simulation and the real world spanning transfer between parallel grippers, dexterous hands, and composite embodiments. KITE successfully achieves zero-shot transfer to structurally different target embodiments, and consistently outperforms state-of-the-art baselines. Project website: this https URL.
13 pages, including appendix. Project website: this https URL
Robotics (cs.RO); Artificial Intelligence (cs.AI)
arXiv:2606.22113 [cs.RO]
(or arXiv:2606.22113v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.22113 Focus to learn more arXiv-issued DOI via DataCite
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