Learning Agile Robotic Locomotion Skills by Imitating Animals
Open accessAbstract
Reproducing the diverse and agile locomotion skills of animals has been a longstanding challenge in robotics. While manually-designed controllers have been able to emulate many complex behaviors, building such controllers involves a time-consuming and difficult development process, often requiring substantial expertise of the nuances of each skill. Reinforcement learning provides an appealing alternative for automating the manual effort involved in the development of controllers. However, designing learning objectives that elicit the desired behaviors from an agent can also require a great deal of skill-specific expertise. In this work, we present an imitation learning system that enables legged robots to learn agile locomotion skills by imitating real-world animals. We show that by leveraging reference motion data, a single learning-based approach is able to automatically synthesize controllers for a diverse repertoire behaviors for legged robots. By incorporating sample efficient domain adaptation techniques into the training process, our system is able to learn adaptive policies in simulation that can then be quickly adapted for real-world deployment. To demonstrate the effectiveness of our system, we train an 18-DoF quadruped robot to perform a variety of agile behaviors ranging from different locomotion gaits to dynamic hops and turns.
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Application of reinforcement learning to legged locomotion has largely been confined to laboratory environments and conditions.
“However, application of RL to legged locomotion has largely been confined to laboratory environments and conditions.”
From Learning quadrupedal locomotion over challenging terrain (2020) · cites this paper as [19]
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A Survey on Reinforcement Learning Methods in Character Animation
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- Authors
- Bin Peng, X., Coumans, E., Zhang, T., Lee, T.-W., Tan, J., & Levine, S.
- Affiliations
- Google (United States) (US)
- Venue
- Robotics: Science and Systems XVI
- Published
- 2020-07-12
- OpenAlex
- W3014488508
- Cited by
- 357
- References
- 70
- Citations by year
- 2026: 11 · 2025: 62 · 2024: 60 · 2023: 69 · 2022: 74 · 2021: 67 · 2020: 13 · 2019: 1
- FWCI
- 101.22
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Robotics: Science and Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Robotic Locomotion and Control
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Keywords
- agile locomotion, legged robots, quadruped robot, imitation learning, Animal locomotion, reinforcement learning, domain adaptation, sample efficiency, controller synthesis, locomotion gaits, sim-to-real transfer
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