表面拘束ポリシー：表面拘束かつ動的に実現可能なロボットスキルの学習
Surface Constraint Policy for Learning Surface-Constrained and Dynamically Feasible Robot Skills
拡散ベースの模倣学習に表面形状の拘束と動的実現可能性を組み込み、自由曲面に沿ったロボット動作を生成する新しいポリシーを提案した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Shuai Ke, Jiexin Zhang, Huan Zhao, Zhiao Wei, Yikun Guo, Jie Pan, Han Ding
分類: cs.RO
原文アブストラクト
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.
