A Cable-Actuated Soft Manipulator for Dexterous Grasping Based on Deep Reinforcement Learning

IF 6.8 Q1 AUTOMATION & CONTROL SYSTEMS Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-10-15 DOI:10.1002/aisy.202470046

Kunyu Zhou, Baijin Mao, Yuzhu Zhang, Yaozhen Chen, Yuyaocen Xiang, Zhenping Yu, Hongwei Hao, Wei Tang, Yanwen Li, Houde Liu, Xueqian Wang, Xiaohao Wang, Juntian Qu

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Abstract

Cable-Actuated Soft Manipulator Based on Deep Reinforcement Learning

In article number 2400112, Juntian Qu and co-workers propose a type of modified TD3 (twin delayed deep deterministic policy gradient) algorithm in combination with LSTM (long short-term memory) neural networks to control the cable-driven soft manipulator. Multi-scenario and multi-task experiments are carried out based on the soft manipulator, such as precisely placing a 6 mm diameter ball into a 10 mm diameter glass bottle and accurately retrieving a shell from within an L-shaped pipe using the soft manipulator.

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基于深度强化学习的用于灵巧抓取的线控软机械手

基于深度强化学习的线控软机械手在编号为 2400112 的文章中，曲俊田及其合作者提出了一种结合 LSTM（长短期记忆）神经网络的改进型 TD3（双延迟深度确定性策略梯度）算法，用于控制线控软机械手。基于软机械手开展了多场景和多任务实验，例如利用软机械手将直径为 6 毫米的球精确放入直径为 10 毫米的玻璃瓶中，以及从 L 型管道中精确取出贝壳。

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