Knowledge Commons of Institute of Automation,CAS
Review on Peg-in-Hole Insertion Technology Based on Reinforcement Learning | |
Shen Liancheng1,2![]() ![]() | |
2024-03 | |
会议名称 | 2023 China Automation Congress (CAC) |
会议日期 | 2023-11 |
会议地点 | Chongqing, China |
摘要 | Peg-in-hole insertion is a critical process in industrial production. Traditional peg-in-hole insertion methods are based on planning the robot's motion trajectory through the analysis of contact models. However, due to the complexity of contact states, it's challenging to establish precise and reliable contact models, leading to poor generalization of these methods. Reinforcement learning is a technique that learns insertion strategies from environmental interactions, avoiding the tedious process of analytical modeling. Thus, it has become a trending direction in the robotics field in recent years. This article aims to survey the mainstream peg-in-hole insertion technologies based on reinforcement learning methods and discuss future research directions. First, we introduce the task requirements for peg-in-hole insertion. Subsequently, a preliminary framework of reinforcement learning algorithms for peg-in-hole insertion is presented. Discussions are then divided into two main categories: traditional reinforcement learning methods (including model-based and model-free methods) and reinforcement learning methods accelerated by prior knowledge (including residual reinforcement learning, reinforcement learning from demonstration, meta-reinforcement learning, and other acceleration techniques). Finally, this article explores several potential future research directions for peg-in-hole insertion technologies based on reinforcement learning. |
关键词 | —Robot Peg-in-hole Insertion Reinforcement Learning Meta-Reinforcement Learning |
学科门类 | Robotics |
URL | 查看原文 |
收录类别 | EI |
七大方向——子方向分类 | 智能机器人 |
国重实验室规划方向分类 | 实体人工智能系统决策-控制 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/57537 |
专题 | 多模态人工智能系统全国重点实验室_机器人理论与应用 |
通讯作者 | Su Jianhua |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences 3.Institute of Spacecraft System Engineering, China Academy of Space Technology |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Shen Liancheng,Su Jianhua,Zhang Xiaodong. Review on Peg-in-Hole Insertion Technology Based on Reinforcement Learning[C],2024. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Review_on_Peg-in-Hol(254KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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