Industrial robots are widely deployed to perform pick-and-place tasks at high speeds to minimize manufacturing time and boost productivity. When dealing with delicate or fragile goods, soft robotic grippers are better end effectors than rigid grippers due to their softness and safe interaction. However, high-speed motion causes the soft robotic gripper to vibrate, leading to damage of the objects or failed grasping. Soft grippers with variable stiffness are considered to be effective in suppressing vibrations by adding damping devices, but it is quite challenging to compromise between stiffness and compliance. In this article, a controller based on deep reinforcement learning is proposed to control the stiffness of the soft robotic gripper, which can accurately suppress the vibration with only a minor influence on its compliance and softness. The proposed controller is a real-time vibration control strategy, which estimates the output of the controller based on the current operating environment. To demonstrate the effectiveness of the proposed controller, experiments were done with a UR5 robotic arm. For different situations, experimental results show that the proposed controller responds quickly and reduces the amplitude of the oscillation substantially.

中文翻译：

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基于强化学习的高速运动中变刚度软夹具的鲁棒抓取。

工业机器人被广泛部署来高速执行拾取和放置任务，以最大限度地减少制造时间并提高生产率。在处理精致或易碎的货物时，由于柔软且交互安全，软机器人抓手比刚性抓手是更好的末端执行器。然而，高速运动会导致软体机器人夹具振动，导致物体损坏或抓取失败。具有可变刚度的软夹具被认为可以通过添加阻尼装置来有效抑制振动，但在刚度和柔度之间进行折衷是相当具有挑战性的。本文提出了一种基于深度强化学习的控制器来控制软体机器人夹具的刚度，能够精确抑制振动，且对其柔顺性和柔软度影响很小。所提出的控制器是一种实时振动控制策略，它根据当前的运行环境估计控制器的输出。为了证明所提出的控制器的有效性，使用 UR5 机械臂进行了实验。对于不同的情况，实验结果表明，所提出的控制器响应速度快，并大大降低了振荡幅度。