用全身接触感知学习变形体集群操作策略，实现真实场景下自动清障。
Deformable Cluster Manipulation via Whole-Arm Policy Learning
- 融合点云与触觉信号，以全身接触意识替代末端执行器控制。
- 在电力线清障任务中成功生成多关节协同的创造性解法。
- 零样本仿真到现实迁移，可处理未知遮挡与动态变化的树枝。
操纵变形体集群具有广泛应用前景，但需依赖丰富的全身接触交互。现有方法受限于真实模型构建能力弱、感知不确定性高及缺乏高效空间抽象。本文提出一种新型无模型策略学习框架，融合3D点云与本体感觉触觉信号，强调全肢体接触意识，突破传统末端执行器模式。采用分布式状态表示结合核均值嵌入，提升训练效率与实时推理能力。此外，提出一种不依赖上下文的遮挡清除启发式方法，用于暴露任务。在电力线路清障场景部署后，智能体生成了利用多个机械臂链接协同去遮挡的创造性策略。最后，实现了零样本仿真到现实的策略迁移，使机械臂能清理真实环境中未知遮挡模式、未见拓扑结构及不确定动力学的树枝。
原文摘要 · Abstract (English)
Manipulating clusters of deformable objects presents a substantial challenge with widespread applicability, but requires contact-rich whole-arm interactions. A potential solution must address the limited capacity for realistic model synthesis, high uncertainty in perception, and the lack of efficient spatial abstractions, among others. We propose a novel framework for learning model-free policies integrating two modalities: 3D point clouds and proprioceptive touch indicators, emphasising manipulation with full body contact awareness, going beyond traditional end-effector modes. Our reinforcement learning framework leverages a distributional state representation, aided by kernel mean embeddings, to achieve improved training efficiency and real-time inference. Furthermore, we propose a novel context-agnostic occlusion heuristic to clear deformables from a target region for exposure tasks. We deploy the framework in a power line clearance scenario and observe that the agent generates creative strategies leveraging multiple arm links for de-occlusion. Finally, we perform zero-shot sim-to-real policy transfer, allowing the arm to clear real branches with unknown occlusion patterns, unseen topology, and uncertain dynamics. Website: https://sites.google.com/view/dcmwap/
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