RMA: Rapid Motor Adaptation for Legged Robots | HTML5
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 rea
RMA: Rapid Motor Adaptation for Legged Robots Ashish Kumar UC Berkeley Zipeng Fu Carnegie Mellon University Deepak Pathak Carnegie Mellon University Jitendra Malik UC Berkeley, Facebook 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 compon
related reading
- State of Robot Learning, December 2025vedder.io
- Precise Manipulation with Efficient Online RLpi.website
- How Claude Performs on Robotics Tasks \ Anthropicanthropic.com
- Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learningarxiv.org
- ProNav: Proprioceptive Traversability Estimation for Legged Robot Navigation in Outdoor Environmentsarxiv.org
- Learning Sim-to-Real Humanoid Locomotion in 15 Minutesyounggyo.me
- A Unified Model for Motion-Conditioned Robot Co-designtransformer-transformer.github.io
- A VLA with Open-World Generalizationpi.website
- Supervised Policy Learning for Real Robotssupervised-robot-learning.github.io
- UniTracker: Learning Universal Whole-Body Motion Tracker for Humanoid Robotsarxiv.org
- Stop Simulating, Start Experiencingpaoloai.substack.com
- Building Worlds That Train Robotsworldlabs.ai