发表机构
Shanghai Jiao Tong University（上海交通大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对扩散策略生成轨迹不连续不平滑的问题，提出LieSpline-DP，在SE(3)上直接生成B样条轨迹并共享边界控制位姿保证C^2连续，在真实机器人任务中降低加加速度并显著提升成功率。
AI 中文摘要
扩散策略（Diffusion Policy, DP）是一种强大的机器人操作模仿学习方法，但其生成的轨迹存在不连续和不平滑的问题。基于样条的动作表示能在单个动作块内促进平滑运动，但现有的基于样条的方法既不能保证跨块的$C^2$连续性，也未考虑$\u200b\u200bSE(3)$的群结构。为此，我们提出了LieSpline-DP，一种李群B样条扩散策略，直接在$\u200b\u200bSE(3)$上生成末端执行器轨迹，并通过共享边界控制位姿来耦合连续的计划，从而确保整个规划轨迹的$C^2$连续性。在三个真实机器人任务中，LieSpline-DP相比DP基线产生了更低的轨迹加加速度和更高的任务成功率。在涉及液体和柔性物体的真实世界任务中，提升尤为显著：在我们的真实机器人实验中，LieSpline-DP在倒水和吊桶挂钩任务上均达到了100%的成功率，而DP基线分别仅达到10%和30%。
英文摘要
Diffusion Policy (DP) is a powerful Learning from Demonstration (LfD) method for robotic manipulation, yet it suffers from discontinuous and non-smooth trajectories. Spline-based action representations promote smooth motion within individual action chunks, but existing spline-based methods neither guarantee cross-chunk $C^2$ continuity nor account for the group structure of $\mathrm{SE}(3)$. We therefore propose LieSpline-DP, a Lie-group B-spline diffusion policy that generates end-effector trajectories directly on $\mathrm{SE}(3)$ and couples consecutive plans by sharing their boundary control poses, ensuring $C^2$ continuity throughout the entire planned trajectory. Across three real-robot tasks, LieSpline-DP produces lower trajectory jerk and higher task success rates than the DP baseline. The gains are particularly pronounced in real-world tasks involving liquids and flexible objects: in our real-robot experiments, LieSpline-DP achieved a 100% success rate on both pouring and bucket hooking, whereas the DP baseline achieved only 10% and 30%, respectively.
Comments8 pages, 6 figures, 3 tables
