用触觉反馈+强化学习，让机械臂在看不清物体时也能稳稳抓取。
Tactile-based Reinforcement Learning for Adaptive Grasping under Observation Uncertainties
- 结合触觉传感与PPO算法动态调整抓取姿态
- 在观测不确定下抓取成功率显著提升
- 适合工业场景中视线受阻的抓取任务
工业场景中的机器人操作常面临观测不确定性，例如管道安装、钢筋铺设和电气安装过程中因遮挡或部分可观测导致物体状态估计不准。传统视觉抓取方法难以保证鲁棒性和适应性。本文提出一种触觉模拟器，支持基于触觉反馈的自适应抓取方法，利用触觉信号与近端策略优化（Proximal Policy Optimization, PPO）强化学习算法动态调整抓取姿态，以应对物体状态估计不准确的情况。仿真结果表明，该方法能有效适应不同抓取条件，显著提升抓取任务的成功率与稳定性。
原文摘要 · Abstract (English)
Robotic manipulation in industrial scenarios such as construction commonly faces uncertain observations in which the state of the manipulating object may not be accurately captured due to occlusions and partial observables. For example, object status estimation during pipe assembly, rebar installation, and electrical installation can be impacted by observation errors. Traditional vision-based grasping methods often struggle to ensure robust stability and adaptability. To address this challenge, this paper proposes a tactile simulator that enables a tactile-based adaptive grasping method to enhance grasping robustness. This approach leverages tactile feedback combined with the Proximal Policy Optimization (PPO) reinforcement learning algorithm to dynamically adjust the grasping posture, allowing adaptation to varying grasping conditions under inaccurate object state estimations. Simulation results demonstrate that the proposed method effectively adapts grasping postures, thereby improving the success rate and stability of grasping tasks.
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