Pick and Place Without Geometric Object Models

Pick and Place Without Geometric Object Models
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无需几何对象模型即可拾取和放置

DOI:
10.1109/icra.2018.8460553
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发表时间:
2018
期刊:
Proceedings of 2018 IEEE International Conference on Robotics and Automation (ICRA
影响因子:
--
通讯作者:
Platt, Robert
Platt, Robert
中科院分区:
--
文献类型:
--
作者:
Gualtieri, Marcus;Pas, Andreas ten;Platt, Robert

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提出了一种基于深度强化学习(RL)的机器人拾取与放置的新方法。尽管大多数机器人操作的深层RL方法都是根据低级别的状态和动作来框架问题,但我们提出了一个更抽象的公式。在这个公式中,动作是手的目标伸展姿势,而状态是这种伸展的历史。我们证明了这种方法可以解决一类具有挑战性的拾取放置和重新定位问题,其中要处理的对象的确切几何形状是未知的。我们的方法需要的唯一信息是:1)机器人在测试时可获得的传感器感知;2)系统针对其进行训练的对象的一般类别的先验知识。我们使用了属于两个不同类别的物体,杯子和瓶子,在模拟和真实硬件上对我们的方法进行了评估。结果显示相对于形状基元基线有了很大的改进。
We propose a novel formulation of robotic pick and place as a deep reinforcement learning (RL) problem. Whereas most deep RL approaches to robotic manipulation frame the problem in terms of low level states and actions, we propose a more abstract formulation. In this formulation, actions are target reach poses for the hand and states are a history of such reaches. We show this approach can solve a challenging class of pick-place and regrasping problems where the exact geometry of the objects to be handled is unknown. The only information our method requires is: 1) the sensor perception available to the robot at test time; 2) prior knowledge of the general class of objects for which the system was trained. We evaluate our method using objects belonging to two different categories, mugs and bottles, both in simulation and on real hardware. Results show a major improvement relative to a shape primitives baseline.
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影响因子: --
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