论文标题
学会为机器人操作组成层次以对象的控制器
Learning to Compose Hierarchical Object-Centric Controllers for Robotic Manipulation
论文作者
论文摘要
操纵任务通常可以分解为并联执行的多个子任务，例如，将对象滑入目标姿势，同时保持与表格的接触。可以通过相对于要操纵的对象定义的任务轴控制器来实现各个子任务，并且可以将一组中心的以对象控制器的形式合并到层次结构中。在先前的作品中，此类组合是手动定义或从示范中学到的。相比之下，我们建议使用强化学习来动态组成以对象为中心的控制器进行操纵任务。模拟和现实世界中的实验表明，所提出的方法如何导致样品效率提高，对新的测试环境的零弹性概括以及仿真到现实的转移而无需进行微调。
Manipulation tasks can often be decomposed into multiple subtasks performed in parallel, e.g., sliding an object to a goal pose while maintaining contact with a table. Individual subtasks can be achieved by task-axis controllers defined relative to the objects being manipulated, and a set of object-centric controllers can be combined in an hierarchy. In prior works, such combinations are defined manually or learned from demonstrations. By contrast, we propose using reinforcement learning to dynamically compose hierarchical object-centric controllers for manipulation tasks. Experiments in both simulation and real world show how the proposed approach leads to improved sample efficiency, zero-shot generalization to novel test environments, and simulation-to-reality transfer without fine-tuning.
