数据类比促进高效的跨机体迁移
Data Analogies Enable Efficient Cross-Embodiment Transfer
AI总结：
研究探讨了如何通过数据类比提升机器人跨机体迁移效率，发现结构化多样性在感知变化中效果显著，而形态变化则更依赖数据类比。
摘要：
通用机器人策略是在多种机器人、场景和视角的演示数据上训练的。然而，如何组织和扩展此类异质数据以真正提升目标设置的性能仍不明确。本文提出：哪种形式的演示数据最有助于跨机器人设置的迁移？我们通过控制实验变化末端执行器形态、机器人平台外观和摄像头视角，并比较单纯扩展演示数据量与系统性扩展多样性效果。模拟实验表明，感知变化如视角最受益于广度多样性，而形态变化则更受益于数据类比，即配对演示在不同机体间对齐场景、任务和/或轨迹。基于模拟结果，我们通过仅改变数据组成，将现实世界跨机体迁移成功率提高22.5%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
