A Data-Centric Alignment Study for Cross-Embodiment Transfer

Cross-embodiment learning offers a compelling way to scale robot learning by transferring knowledge from other source embodiments to a target embodiment. However, a shared end-effector (EEF) format does not guarantee data compatibility, as embodiment-specific differences can exist even for similar manipulation behaviors. We take a data-centric perspective, studying how explicit data alignment affects this transfer with an otherwise unchanged policy. Specifically, we consider four aspects, including frame coordinate, action mapping across arm configurations, Cartesian states, and observation viewpoints. We evaluate the cross-embodiment transfer through a carefully designed setting around source-only tasks tested on the target embodiment. In real-world skill transfer, data alignment alone achieves 90.5% success, compared with 34.8% for naive data mixing. Further analyses show the benefits of shared tasks across embodiment, indicate that alignment benefits extend beyond a particular embodiment, and provide insights into the roles of different alignment components, as well as the choice of strategies within each component. These findings identify cross-embodiment data compatibility as an important consideration in combining heterogeneous robot data and provide a practical data-centric baseline for cross-embodiment transfer. Real-world videos are in supplementary materials.