Knowledge Commons of Institute of Automation,CAS
Active Pushing for Better Grasping in Dense Clutter with Deep Reinforcement Learning | |
Lu, Ning1,2; Lu, Tao1; Cai, Yinghao1; Wang, shuo1 | |
2021 | |
会议名称 | 2020 Chinese Automation Congress (CAC) |
会议日期 | 6-8 Nov. 2020 |
会议地点 | Shanghai, China |
摘要 | Robotic grasping in unstructured dense clutter remains a challenging task and has always been a key research direction in the field of robotics. In this paper, we propose a novel robotic grasping system that could use the synergies between pushing and grasping actions to automatically grasp the objects in dense clutter. Our method involves using fully convolutional action-value functions (FCAVF) to map from visual observations to two action-value tables in a Q-learning framework. These two value tables infer the utility of pushing and grasping actions, and the highest value with the corresponding location and orientation means the best place to execute action for the end effector. For better grasping, we introduce an active pushing mechanism based on a new metric, called Dispersion Degree, which describes how spread out the objects are in the environment. Then we design a coordination mechanism to apply the synergies of different actions based on the action-values and dispersion degree of the objects and make the grasps more effective. Experimental results show that our proposed robotic grasping system can greatly improve the robotic grasping success rate in dense clutter and also has the capability to be generalized to the new scenarios. |
收录类别 | EI |
语种 | 英语 |
七大方向——子方向分类 | 智能机器人 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44408 |
专题 | 智能机器人系统研究 |
通讯作者 | Lu, Tao |
作者单位 | 1.Research Center on Intelligent Robotic Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China 2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China |
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 | Lu, Ning,Lu, Tao,Cai, Yinghao,et al. Active Pushing for Better Grasping in Dense Clutter with Deep Reinforcement Learning[C],2021. |
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EI.pdf(1435KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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