Rapidly Adaptable Legged Robots via Evolutionary Meta-Learning

• URL: http://arxiv.org/abs/2003.01239v3
• Date: Thu, 30 Jul 2020 00:22:35 GMT
• Title: Rapidly Adaptable Legged Robots via Evolutionary Meta-Learning
• Authors: Xingyou Song, Yuxiang Yang, Krzysztof Choromanski, Ken Caluwaerts, Wenbo Gao, Chelsea Finn, Jie Tan
• Abstract summary: We present a new meta-learning method that allows robots to quickly adapt to changes in dynamics. Our method significantly improves adaptation to changes in dynamics in high noise settings. We validate our approach on a quadruped robot that learns to walk while subject to changes in dynamics.
• Score: 65.88200578485316
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning adaptable policies is crucial for robots to operate autonomously in our complex and quickly changing world. In this work, we present a new meta-learning method that allows robots to quickly adapt to changes in dynamics. In contrast to gradient-based meta-learning algorithms that rely on second-order gradient estimation, we introduce a more noise-tolerant Batch Hill-Climbing adaptation operator and combine it with meta-learning based on evolutionary strategies. Our method significantly improves adaptation to changes in dynamics in high noise settings, which are common in robotics applications. We validate our approach on a quadruped robot that learns to walk while subject to changes in dynamics. We observe that our method significantly outperforms prior gradient-based approaches, enabling the robot to adapt its policy to changes based on less than 3 minutes of real data.

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