SPACE: Enabling Learning from Cross-Robot Data Toward Generalist Policies

2026-06-22

AI Summary

This work addresses the challenge of transferring motion policies across robots with diverse morphologies and dynamics. To enable cross-robot policy generalization, the authors propose representing actions as incremental changes in Cartesian-space states and introduce the State Prediction and Adaptive Command Execution (SPACE) framework. This framework leverages geometric end-effector displacement prediction combined with lightweight action adapters to bridge embodiment differences. Notably, it is the first approach to uniformly handle dynamic discrepancies across three levels: between distinct robot embodiments, among hardware variants of the same morphology, and within a single robot under runtime condition changes. Experiments demonstrate that behavior cloning trained on this universal action representation significantly outperforms direct control command prediction when deployed across platforms, maintaining robustness under variations in control frequency, payload, and controller gains.

Abstract

In robot learning, scaling training datasets across diverse embodiments and environments has become a dominant paradigm for learning generalizable robot policies. These policies are commonly trained via behavior cloning to imitate actions from pre-collected demonstrations. However, since robot actions are tied to the dynamics of the data collection robot, different robots may require different actions to achieve the same motion. This discrepancy hinders both policy training and deployment across diverse robots. To address this, we propose using Cartesian state delta as a universal action representation across robots, and introduce State Prediction and Adaptive Command Execution (SPACE) framework. SPACE handles robot dynamics variation at three levels: across different embodiments, across hardware units of the same embodiment, and within a single robot during operation. It consists of two components: (i) a Cartesian state delta policy that predicts geometric end-effector displacement, and (ii) Action Adapter, which converts the predicted Cartesian state delta into robot-specific control commands. Experiments show that SPACE substantially outperforms policies that directly predict control commands when learning from data collected across different embodiments and across hardware units of the same embodiment. SPACE also remains robust under dynamics shifts at deployment, including changes in control frequency, object weight, and controller gains. The project page is available at http://haeone.site/space-website/.

Problem

• robot learning
• generalist policies
• cross-robot data
• action representation
• dynamics variation

Innovation

• Cartesian state delta
• cross-robot generalization
• behavior cloning
• action representation
• robot learning