通过传感器空间学习实现软机械臂在受限环境下的鲁棒运动控制
Sensor-Space Based Robust Kinematic Control of Redundant Soft Manipulator by Learning
- 基于强化学习与模仿学习的双策略框架，分场景训练控制策略
- 实测在未知负载下仍能精准路径追踪与抓取操作
- 支持零样本跨域部署，有效缓解仿真到现实的偏差
冗余软机械臂具有内在柔性和高自由度，有利于安全交互与灵活执行任务。然而，有效的运动控制仍面临挑战，需应对未知外部载荷引起的形变，并避免因不当空域调节导致的执行器饱和，尤其在狭小空间中更为显著。本文提出一种基于传感器空间的仿习性运动控制框架（SS-ILKC），以实现执行器饱和与环境约束下的鲁棒控制。采用双学习策略：在仿真中基于强化学习原理训练多目标传感器空间控制框架，构建开放空间下的稳健策略；在受限空间中，利用生成对抗式模仿学习，从稀疏专家示范中高效学习策略。为实现零样本真实部署，提出预处理的仿真到现实迁移机制，有效缓解仿真-现实差距并精确刻画执行器饱和极限。实验表明，该方法可成功控制气动驱动软机械臂，在未知载荷条件下完成狭小空间内的精确路径跟踪与物体操作。
原文摘要 · Abstract (English)
The intrinsic compliance and high degree of freedom (DoF) of redundant soft manipulators facilitate safe interaction and flexible task execution. However, effective kinematic control remains highly challenging, as it must handle deformations caused by unknown external loads and avoid actuator saturation due to improper null-space regulation - particularly in confined environments. In this paper, we propose a Sensor-Space Imitation Learning Kinematic Control (SS-ILKC) framework to enable robust kinematic control under actuator saturation and restrictive environmental constraints. We employ a dual-learning strategy: a multi-goal sensor-space control framework based on reinforcement learning principle is trained in simulation to develop robust control policies for open spaces, while a generative adversarial imitation learning approach enables effective policy learning from sparse expert demonstrations for confined spaces. To enable zero-shot real-world deployment, a pre-processed sim-to-real transfer mechanism is proposed to mitigate the simulation-to-reality gap and accurately characterize actuator saturation limits. Experimental results demonstrate that our method can effectively control a pneumatically actuated soft manipulator, achieving precise path-following and object manipulation in confined environments under unknown loading conditions.
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