Unsupervised Skill Discovery for Robotic Manipulation through Automatic Task Generation
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Hierarchical Reinforcement LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Unsupervised Reinforcement Learning for Transferable Manipulation Skill Discovery
Current reinforcement learning (RL) in robotics often experiences difficulty in generalizing to new downstream tasks due to the innate task-specific training paradigm. To alleviate it, unsupervised RL, a framework that p…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Unsupervised Reinforcement LearningControllability-Aware Unsupervised Skill Discovery
One of the key capabilities of intelligent agents is the ability to discover useful skills without external supervision. However, the current unsupervised skill discovery methods are often limited to acquiring simple, ea…
SLIM: Skill Learning with Multiple Critics
Self-supervised skill learning aims to acquire useful behaviors that leverage the underlying dynamics of the environment. Latent variable models, based on mutual information maximization, have been successful in this tas…
Hierarchical Reinforcement LearningLipschitz-constrained Unsupervised Skill Discovery
We study the problem of unsupervised skill discovery, whose goal is to learn a set of diverse and useful skills with no external reward. There have been a number of skill discovery methods based on maximizing the mutual …
DiversityMuJoCoVariational Curriculum Reinforcement Learning for Unsupervised Discovery of Skills
Mutual information-based reinforcement learning (RL) has been proposed as a promising framework for retrieving complex skills autonomously without a task-oriented reward function through mutual information (MI) maximizat…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Robot Navigation