表面约束策略：学习受表面约束且动态可行的机器人技能
Surface Constraint Policy for Learning Surface-Constrained and Dynamically Feasible Robot Skills
AI总结：
提出表面约束策略（SCP），通过二维加权高斯核编码表面几何约束，结合扩散策略和基于相似性的动作映射生成动态可行的表面约束运动，解决了自由曲面约束下动作随机性和接触不稳定的问题。
摘要：
基于扩散的模仿学习方法在机器人灵巧操作任务中取得了快速进展。然而，当应用于涉及复杂自由曲面约束的任务时，由于缺乏显式的表面几何约束建模和动态可行性问题，它们存在局限性，导致随机动作生成无法实现可靠的表面对齐和维持稳定接触。为了解决这些局限性，我们提出了一种新颖的表面约束策略（SCP），用于基于人类演示和实时视觉观察生成满足自由曲面约束的机器人动作。首先，使用从演示中推导出的二维加权高斯核函数对表面几何约束进行编码。基于编码的表面几何约束，使用基于扩散的策略从多模态感知输入（包括视觉观察和机器人状态反馈）中推断任务级动作意图。这些意图通过基于相似性的动作映射方法进一步转化为表面约束的动态运动基元（DMP），从而实现平滑且柔顺的运动执行。SCP实现了结构化表面几何意图和动态可接受动作的生成。所提出的方法在多个表面操作任务上进行了验证，并与现有技术进行了比较。实验结果表明，在表面约束下，该方法具有优越的任务成功率和接触稳定性。
英文摘要：
Diffusion-based imitation learning methods have driven rapid progress in robot dexterous manipulation tasks. However, they have limitations when applied to tasks that involve complex free-form surface constraints because of their lack of explicit surface geometry constraint modeling and the dynamic feasibility issue, resulting in stochastic action generation that fails to achieve reliable surface alignment and maintain stable contact. To address these limitations, we propose a novel surface constraint policy (SCP) for generating robot actions that satisfy free-form surface constraints on the basis of human demonstrations and real-time visual observations. First, the surface geometry constraint is encoded using a two-dimensional weighted Gaussian kernel function that is derived from demonstrations. Building on the encoded surface geometry constraints, the diffusion-based policy is used to infer task-level action intentions from multimodal sensory inputs, including visual observations and robot state feedback. These intentions are further transformed into surface-constrained dynamic movement primitives (DMPs) through a similarity-based action mapping method, thereby enabling smooth and compliant motion execution. The SCP achieves generation of structured surface geometric intent and dynamically admissible actions. The proposed method is validated on multiple surface manipulation tasks and compared with existing techniques. The experimental results demonstrate superior task success rates and contact stability under surface constraints.
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。
