论文标题
用于机器人组装的操纵原始序列的学习序列
Learning Sequences of Manipulation Primitives for Robotic Assembly
论文作者
论文摘要
本文探讨了这样一个想法，即熟练的组装最好用作操纵原始的动态序列，并且可以通过增强学习自动发现此类序列。操纵原语（例如“向下移动直到接触”，“沿X上滑动”，同时保持与表面的接触”，具有足够的复杂性来保持搜索树的浅色，但通用足以使其跨越各种汇编任务。此外，与更基本的动作相比，操纵原始词的其他“语义”使它们在Sim2real和模型/环境变化和不确定性方面都更加健壮。在模拟中学习了策略，然后转移到物理平台上。直接的SIM2REAL转移（不实际进行）在具有挑战性的组装任务上实现了出色的成功率，例如带有0.04 mm间隙的圆形PEG插入或具有大孔位置/方向估计误差的正方形PEG插入。
This paper explores the idea that skillful assembly is best represented as dynamic sequences of Manipulation Primitives, and that such sequences can be automatically discovered by Reinforcement Learning. Manipulation Primitives, such as "Move down until contact", "Slide along x while maintaining contact with the surface", have enough complexity to keep the search tree shallow, yet are generic enough to generalize across a wide range of assembly tasks. Moreover, the additional "semantics" of the Manipulation Primitives make them more robust in sim2real and against model/environment variations and uncertainties, as compared to more elementary actions. Policies are learned in simulation, and then transferred onto a physical platform. Direct sim2real transfer (without retraining in real) achieves excellent success rates on challenging assembly tasks, such as round peg insertion with 0.04 mm clearance or square peg insertion with large hole position/orientation estimation errors.
