Publication
An adaptable approach to learn realistic legged locomotion without examples
Conference Article
Conference
IEEE International Conference on Robotics and Automation (ICRA)
Edition
2022
Pages
4671-4678
Doc link
http://dx.doi.org/10.1109/ICRA46639.2022.9812441
File
Authors
Projects associated
Abstract
Learning controllers that reproduce legged locomotion in nature has been a long-time goal in robotics and computer graphics. While yielding promising results, recent approaches are not yet flexible enough to be applicable to legged systems of different morphologies. This is partly because they often rely on precise motion capture references or elaborate learning environments that ensure the naturality of the emergent locomotion gaits but prevent generalization. This work proposes a generic approach for ensuring realism in locomotion by guiding the learning process with the spring-loaded inverted pendulum model as a reference. Leveraging on the exploration capacities of Reinforcement Learning (RL), we learn a control policy that fills in the information gap between the template model and full-body dynamics required to maintain stable and periodic locomotion. The proposed approach can be applied to robots of different sizes and morphologies and adapted to any RL technique and control architecture. We present experimental results showing that even in a model-free setup and with a simple reactive control architecture, the learned policies can generate realistic and energy-efficient locomotion gaits for a bipedal and a quadrupedal robot. And most importantly, this is achieved without using motion capture, strong constraints in the dynamics or kinematics of the robot, nor prescribing limb coordination. We provide supplemental videos for qualitative analysis of the naturality of the learned gaits.
Categories
robots.
Author keywords
Reinforcement Learning, Legged Locomotion
Scientific reference
D.F. Ordoñez, A. Agudo, F. Moreno-Noguer and M. Martin. An adaptable approach to learn realistic legged locomotion without examples, 2022 IEEE International Conference on Robotics and Automation, 2022, Philadelphia, Pennsylvania, USA, pp. 4671-4678.
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