用数据类比提升机器人跨平台迁移效果
Data Analogies Enable Efficient Cross-Embodiment Transfer
- 通过配对不同机器人形态的数据，构建数据类比来增强迁移能力
- 实测显示跨平台成功率平均提升22.5%，优于单纯增加数据量
- 适合做通用机器人策略训练，尤其在多形态设备间迁移时
通用机器人策略通常在多种机器人、场景和视角下收集的示范数据上训练。但如何组织与扩展这类异构数据以真正提升目标设置下的性能仍不明确。本文探究：何种示范数据最有助于跨机器人配置的迁移？通过控制实验，改变末端执行器形态、机器人外观和相机视角，对比单纯增加示范数量与系统性拓宽多样性的影响。模拟实验表明，感知变化（如视角）从广义多样性中获益最大，而形态变化则更依赖数据类比——即在不同机器人形态间对齐场景、任务或轨迹的成对示范。基于此，仅调整数据构成，真实世界跨平台迁移成功率平均提升22.5%，优于大规模非配对数据集。
原文摘要 · Abstract (English)
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.
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