Knowledge Commons of Institute of Automation,CAS
Learning Individual Features to Decompose State Space for Robotic Skill Learning | |
Fengyi Zhang1,2; Fangzhou Xiong1,2; Zhiyong Liu1,2,3 | |
2020-08 | |
会议名称 | Chinese Control And Decision Conference(CCDC) |
会议日期 | 2020-8 |
会议地点 | Online |
摘要 | Due to suffering from the diversity and complexity of robotic tasks in continuous domains, robotic skill learning is the most challenging issue in this area, especially for robots with high-dimensional state spaces. To learn structured policies for continuous control, the graph neural networks (GNN) was previously applied to incorporate explicitly the robot structure into the policy network. In this work, we tackle the problem of robotic skill learning in high-dimensional state space with the help of graph neural networks. Instead of utilizing a general purpose multi-layer perceptron (MLP) as a unified controller to output actions for all joints of the robot, we construct a separate controller for each joint of the robot by using the individual features that have been extracted by GNN model. Empirical results on simulated continuous systems, including applications to PR2 task and Centipede task, demonstrate that the proposed framework can achieve satisfactory learning performance, and more importantly, it significantly reduces the parameters of the policy network. |
关键词 | Robotic Skill Learning Graph Neural Networks State Decomposition |
语种 | 英语 |
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 | 其他 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/50843 |
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 | Zhiyong Liu |
作者单位 | 1.State Key Lab of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Science, Beijing, 100190, China 2.School of Artificial Intelligence, University of Chinese Academy of Sciences (UCAS), Beijing, 100049, China 3.CAS Centre for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China |
推荐引用方式 GB/T 7714 | Fengyi Zhang,Fangzhou Xiong,Zhiyong Liu. Learning Individual Features to Decompose State Space for Robotic Skill Learning[C],2020. |
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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 | ||
发表版(CCDC).pdf(622KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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