多机器人协作抓取运输，用三元力表示提升环境适应性。
Cooperative Grasping and Transportation using Multi-agent Reinforcement Learning with Ternary Force Representation
- 用三元力表示替代原始力信号，增强对环境变化的鲁棒性。
- 在仿真和真实场景中均有效应对抓取力度、物体尺寸与形状变化。
- 适合需要无通信、高可靠性的多机器人协同任务场景。
协作抓取与运输需高效协调完成任务。本研究聚焦于利用力觉反馈的协同机制，即机器人通过传感器感知其他机器人施加于物体上的力以实现协调。相比显式通信，该方法避免延迟与中断；然而，力觉信号高度敏感，易受抓取环境变化（如抓取力度、姿态、物体大小与几何形状）干扰，进而影响协调效果。为此，本文提出基于多智能体强化学习（MARL）的三元力表示方法，该表示在抓取环境变化下保持一致性。仿真与真实世界实验表明，所提方法对抓取力度、物体尺寸与几何形状变化以及模拟到现实的差异均具有强鲁棒性。
原文摘要 · Abstract (English)
Cooperative grasping and transportation require effective coordination to complete the task. This study focuses on the approach leveraging force-sensing feedback, where robots use sensors to detect forces applied by others on an object to achieve coordination. Unlike explicit communication, it avoids delays and interruptions; however, force-sensing is highly sensitive and prone to interference from variations in grasping environment, such as changes in grasping force, grasping pose, object size and geometry, which can interfere with force signals, subsequently undermining coordination. We propose multi-agent reinforcement learning (MARL) with ternary force representation, a force representation that maintains consistent representation against variations in grasping environment. The simulation and real-world experiments demonstrate the robustness of the proposed method to changes in grasping force, object size and geometry as well as inherent sim2real gap.
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