Generalist robot policies are trained on demonstrations collected across a wide variety of robots, scenes, and viewpoints. Yet it remains unclear how to best organize and scale such heterogeneous data so that it genuinely improves performance in a given target setting. In this work, we ask: what form of demonstration data is most useful for enabling transfer across robot set-ups? We conduct controlled experiments that vary end-effector morphology, robot platform appearance, and camera perspective, and compare the effects of simply scaling the number of demonstrations against systematically broadening the diversity in different ways. Our simulated experiments show that while perceptual shifts such as viewpoint benefit most from broad diversity, morphology shifts benefit far less from unstructured diversity and instead see the largest gains from data analogies, i.e. paired demonstrations that align scenes, tasks, and/or trajectories across different embodiments. Informed by the simulation results, we improve real-world cross-embodiment transfer success by an average of $22.5\%$ over large-scale, unpaired datasets by changing only the composition of the data.

翻译：通用机器人策略通过收集各种机器人、场景和视角下的演示数据进行训练。然而，如何最佳地组织和扩展这种异构数据以真正提升特定目标场景中的性能，目前仍不明确。在本研究中，我们探讨：哪种形式的演示数据对于实现跨机器人设置的迁移最为有效？我们通过控制实验，改变末端执行器形态、机器人平台外观和相机视角，并比较单纯增加演示数量与以不同方式系统性地扩展多样性之间的效果。我们的仿真实验表明，虽然视角等感知偏移最受益于广泛的多样性，但形态偏移从非结构化多样性中获益甚微，反而通过数据类比（即在不同具身之间对齐场景、任务和/或轨迹的配对演示）获得最大增益。基于仿真结果，我们仅通过改变数据构成，将真实世界跨具身迁移的成功率相较于大规模非配对数据集平均提升了$22.5\%$。