通过自动生成任务发现可组合的机器人操作技能。
Unsupervised Skill Discovery for Robotic Manipulation through Automatic Task Generation
- 自动生成多样任务，用非监督方式学习可复用操作行为。
- 在仿真和真实机器人上均能解决未见过的抓取任务。
- 适合研究机器人技能迁移与层次强化学习的开发者。
学习与物体交互的技能对机器人操作至关重要，这些技能可作为解决各类操作任务的有效先验。本文提出一种新的技能学习方法，通过自主生成大量多样化的任务来发现可组合的行为。该方法学习到的技能使机器人能够稳定、鲁棒地与环境中的物体交互。所发现的行为被嵌入基础动作中，可通过层次强化学习进行组合以解决未见过的操作任务。具体采用非对称自对弈（Asymmetric Self-Play）发现行为，并利用乘法组合策略（Multiplicative Compositional Policies）进行嵌入。与现有技能学习基线对比，本方法获得的技能更具交互性；且在仿真和真实机器人平台上均成功应用于未见过的操作任务。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。