通过自动任务生成实现机器人操作的无监督技能发现
Unsupervised Skill Discovery for Robotic Manipulation through Automatic Task Generation
AI总结：
针对机器人操作技能学习需求，提出一种通过自动生成大量任务、结合非对称自博弈与乘法组合策略的无监督技能发现方法，可生成交互性更强的可组合技能，适配仿真与真实平台的未知操作任务。
摘要：
学习与物体交互的技能对机器人操作至关重要，这类技能确实可作为解决各类操作任务的高效先验。我们提出一种新颖的技能学习方法，通过求解大量自主生成的多样化任务来发现可组合的行为。该方法学习的技能能让机器人持续、稳健地与环境中的物体交互。发现的行为被嵌入到基元中，这些基元可通过分层强化学习（Hierarchical Reinforcement Learning）组合起来，以求解未见过的操作任务。具体而言，我们利用非对称自博弈（Asymmetric Self-Play）发现行为，并使用乘法组合策略（Multiplicative Compositional Policies）嵌入这些行为。我们将该方法与多个技能学习基线进行比较，发现我们的技能交互性更强。此外，所学技能可用于求解一系列未见过的操作任务，既适用于仿真环境，也适用于真实机器人平台。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
