论文标题
目标辅助行为者批评6D机器人抓钩与点云
Goal-Auxiliary Actor-Critic for 6D Robotic Grasping with Point Clouds
论文作者
论文摘要
6D机器人抓握超越自上而下的垃圾箱场景是一项艰巨的任务。先前基于6D Grasp合成与机器人运动计划的解决方案通常在开环设置中运行，该环境对GRASP合成误差很敏感。在这项工作中，我们提出了一种新方法，用于学习6D抓握的闭环控制策略。我们的策略将对象的分段点云从以上为中心的相机作为输入，并输出机器人抓手的连续6D控制动作以掌握对象。我们结合了模仿学习和强化学习，并引入了一种用于政策学习的目标辅助行为者 - 批评算法。我们证明，我们学到的政策可以集成到桌面6D抓地系统和人类机器人切换系统中，以改善看不见的物体的抓地力。我们的视频和代码可以在https://sites.google.com/view/gaddpg上找到。
6D robotic grasping beyond top-down bin-picking scenarios is a challenging task. Previous solutions based on 6D grasp synthesis with robot motion planning usually operate in an open-loop setting, which are sensitive to grasp synthesis errors. In this work, we propose a new method for learning closed-loop control policies for 6D grasping. Our policy takes a segmented point cloud of an object from an egocentric camera as input, and outputs continuous 6D control actions of the robot gripper for grasping the object. We combine imitation learning and reinforcement learning and introduce a goal-auxiliary actor-critic algorithm for policy learning. We demonstrate that our learned policy can be integrated into a tabletop 6D grasping system and a human-robot handover system to improve the grasping performance of unseen objects. Our videos and code can be found at https://sites.google.com/view/gaddpg .
