RMA: Rapid Motor Adaptation for Legged Robots

• URL: http://arxiv.org/abs/2107.04034v1
• Date: Thu, 8 Jul 2021 17:59:59 GMT
• Title: RMA: Rapid Motor Adaptation for Legged Robots
• Authors: Ashish Kumar, Zipeng Fu, Deepak Pathak, Jitendra Malik
• Abstract summary: This paper presents Rapid Motor Adaptation (RMA) algorithm to solve this problem of real-time online adaptation in quadruped robots. RMA is trained completely in simulation without using any domain knowledge like reference trajectories or predefined foot trajectory generators. We train RMA on a varied terrain generator using bioenergetics-inspired rewards and deploy it on a variety of difficult terrains.
• Score: 71.61319876928009
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Successful real-world deployment of legged robots would require them to adapt in real-time to unseen scenarios like changing terrains, changing payloads, wear and tear. This paper presents Rapid Motor Adaptation (RMA) algorithm to solve this problem of real-time online adaptation in quadruped robots. RMA consists of two components: a base policy and an adaptation module. The combination of these components enables the robot to adapt to novel situations in fractions of a second. RMA is trained completely in simulation without using any domain knowledge like reference trajectories or predefined foot trajectory generators and is deployed on the A1 robot without any fine-tuning. We train RMA on a varied terrain generator using bioenergetics-inspired rewards and deploy it on a variety of difficult terrains including rocky, slippery, deformable surfaces in environments with grass, long vegetation, concrete, pebbles, stairs, sand, etc. RMA shows state-of-the-art performance across diverse real-world as well as simulation experiments. Video results at https://ashish-kmr.github.io/rma-legged-robots/

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