让机器人跨形态迁移技能，实现不同机械臂和夹爪间的通用操作学习。
CEI: A Unified Interface for Cross-Embodiment Visuomotor Policy Learning in 3D Space
- 基于功能相似性对齐不同机器人轨迹，用方向切比雪夫距离量化匹配度。
- 在仿真中成功迁移至16种不同形态，真实世界任务平均转移率达82.4%。
- 适用于多形态机器人、跨设备技能迁移，适合通用机器人系统研发者。
基于大规模操作数据训练的机器人基础模型虽展现出泛化能力，但常因数据偏差而过度依赖特定视角、机械臂及平行夹爪。为解决此问题，本文提出跨形态接口（ extit{Cross-Embodiment Interface, CEI}），支持不同机械臂与末端执行器间示范数据与策略的迁移。CEI引入“功能相似性”概念，通过方向切比雪夫距离量化，经梯度优化对齐轨迹，并合成未见形态的观测与动作。实验表明，CEI可将Franka Panda数据迁移到3个任务中的16种仿真形态，且在真实世界中实现UR5+AG95与UR5+Xhand之间的双向迁移，覆盖6个任务，平均转移率高达82.4%。此外，该框架还可扩展空间泛化与多模态运动生成能力。
原文摘要 · Abstract (English)
Robotic foundation models trained on large-scale manipulation datasets have shown promise in learning generalist policies, but they often overfit to specific viewpoints, robot arms, and especially parallel-jaw grippers due to dataset biases. To address this limitation, we propose Cross-Embodiment Interface (\CEI), a framework for cross-embodiment learning that enables the transfer of demonstrations across different robot arm and end-effector morphologies. \CEI introduces the concept of \textit{functional similarity}, which is quantified using Directional Chamfer Distance. Then it aligns robot trajectories through gradient-based optimization, followed by synthesizing observations and actions for unseen robot arms and end-effectors. In experiments, \CEI transfers data and policies from a Franka Panda robot to \textbf{16} different embodiments across \textbf{3} tasks in simulation, and supports bidirectional transfer between a UR5+AG95 gripper robot and a UR5+Xhand robot across \textbf{6} real-world tasks, achieving an average transfer ratio of 82.4\%. Finally, we demonstrate that \CEI can also be extended with spatial generalization and multimodal motion generation capabilities using our proposed techniques. Project website: https://cross-embodiment-interface.github.io/
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