Learning skills that interact with objects is of major importance for robotic manipulation. These skills can indeed serve as an efficient prior for solving various manipulation tasks. We propose a novel Skill Learning approach that discovers composable behaviors by solving a large and diverse number of autonomously generated tasks. Our method learns skills allowing the robot to consistently and robustly interact with objects in its environment. The discovered behaviors are embedded in primitives which can be composed with Hierarchical Reinforcement Learning to solve unseen manipulation tasks. In particular, we leverage Asymmetric Self-Play to discover behaviors and Multiplicative Compositional Policies to embed them. We compare our method to Skill Learning baselines and find that our skills are more interactive. Furthermore, the learned skills can be used to solve a set of unseen manipulation tasks, in simulation as well as on a real robotic platform.

本研究解决了机器人操作中技能学习的挑战，提出了一种新颖的技能学习方法，通过自动生成大量多样化任务来发现可组合的行为。研究结果表明，所学习的技能能帮助机器人稳健地与环境中的物体互动，并成功应用于未见的操作任务中，具有重要的实际应用潜力。

通过自动任务生成的无监督技能发现用于机器人操作