CASIA OpenIR  > 智能机器人系统研究
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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