Related papers: Unsupervised Skill Discovery for Robotic Manipulation through Automatic Task Generation

Unsupervised Skill Discovery for Robotic Manipulation through Automatic Task Generation

URL: http://arxiv.org/abs/2410.04855v1
Date: Mon, 7 Oct 2024 09:19:13 GMT
Title: Unsupervised Skill Discovery for Robotic Manipulation through Automatic Task Generation
Authors: Paul Jansonnie, Bingbing Wu, Julien Perez, Jan Peters,
Abstract summary: We propose a Skill Learning approach that discovers composable behaviors by solving a large number of autonomously generated tasks. Our method learns skills allowing the robot to consistently and robustly interact with objects in its environment. The learned skills can be used to solve a set of unseen manipulation tasks, in simulation as well as on a real robotic platform.
Score: 17.222197596599685
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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