Learning 6-DoF Grasping and Pick-Place Using Attention Focus
Learning 6-DoF Grasping and Pick-Place Using Attention Focus
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发表时间：
2018-06
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通讯作者：
Marcus Gualtieri;Robert W. Platt
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作者：
Marcus Gualtieri;Robert W. Platt
我们解决了一类操作问题，其中机器人用深度传感器感知场景，并可以在六个自由度的空间中移动其末端执行器- 3D位置和方向。我们的方法是将问题表述为具有抽象但普遍适用的状态和动作表示的马尔可夫决策过程（MDP）。找到一个好的MDP解决方案需要对允许的操作添加约束。我们开发了一组特定的约束，称为分层$\text{SE}(3)$ sampling (HSE3S)，它使机器人学习一系列凝视，将注意力集中在场景中与任务相关的部分。我们在模拟和真实机器人上演示了我们的方法在三个具有挑战性的拾取任务（在杂乱和非平凡的地方有新的物体）上的有效性，尽管所有的训练都是在模拟中完成的。
We address a class of manipulation problems where the robot perceives the scene with a depth sensor and can move its end effector in a space with six degrees of freedom -- 3D position and orientation. Our approach is to formulate the problem as a Markov decision process (MDP) with abstract yet generally applicable state and action representations. Finding a good solution to the MDP requires adding constraints on the allowed actions. We develop a specific set of constraints called hierarchical $\text{SE}(3)$ sampling (HSE3S) which causes the robot to learn a sequence of gazes to focus attention on the task-relevant parts of the scene. We demonstrate the effectiveness of our approach on three challenging pick-place tasks (with novel objects in clutter and nontrivial places) both in simulation and on a real robot, even though all training is done in simulation.