Cross-Embodiment Transfer via Behavior-Aligned Representations

2026-07-29

AI Summary

This work addresses the challenge of policy transfer across robotic morphologies, which is hindered by embodiment differences that limit the generalization of imitation learning. To overcome this, the authors propose leveraging embodiment-invariant behavioral alignment representations—such as end-effector trajectories, object bounding boxes, and language-based action descriptions—to construct a unified vision-language-action (VLA) model capable of integrating multi-embodiment data and enabling effective cross-embodiment transfer. The approach presents the first systematic evaluation of behavioral alignment representations in sim-to-real transfer, demonstrating substantial performance gains on a newly introduced simulation benchmark. When deployed on real robots, the method improves task success rates by 28% and further enables training augmentation using unlabeled demonstration data lacking action annotations.

Abstract

Recent progress in large-scale imitation learning for robot manipulation has been driven by leveraging datasets across a wide range of robot embodiments. However, achieving significant cross-embodiment transfer is often still challenging. In this work, we study the role of using behavior-aligned representations (e.g., object bounding boxes, language motions, end-effector traces of robot motion) in vision-language-action (VLA) models to promote cross-embodiment transfer. We hypothesize that by possessing invariances across embodiments while being predictive of robot actions, these representations can help unify large-scale cross-embodiment data to enhance transfer. To assess our hypothesis, we develop a simulation-based benchmark designed to assess transfer with diverse cross-embodiment data to new embodiments. Using this benchmark, we compare different representations and ways of incorporating them. We identify that end-effector traces can be particularly beneficial for transfer, representations are generally more useful with larger prior datasets, and can be used to benefit from action-free data. We also demonstrate that they can enhance sim-to-real cross-embodiment transfer, improving task completion progress of real robot policies pre-trained on simulation data by 28%. We provide videos of our evaluations at our website: https://ajaysridhar.com/barx/.

Problem

• cross-embodiment transfer
• robot manipulation
• imitation learning
• vision-language-action models

Innovation

• behavior-aligned representations
• cross-embodiment transfer
• vision-language-action models
• end-effector traces
• sim-to-real transfer