Safe and Adaptive 3-D Locomotion via Constrained Task-Space Imitation Learning

Safe and Adaptive 3-D Locomotion via Constrained Task-Space Imitation Learning

Ding, J. (TU Delft Learning & Autonomous Control)
Lam, Tin Lun (Chinese University of Hong Kong; Shenzhen Institute of Artificial Intelligence and Robotics for Society)
Ge, Ligang (Ubtech Robotics Corporation)
Pang, Jianxin (Ubtech Robotics Corporation)
Huang, Yanlong (University of Leeds)

2023

Bipedal locomotion has been widely studied in recent years, where passive safety (i.e., a biped rapidly brakes without falling) is deemed to be a pivotal problem. To realize safe 3-D walking, existing works resort to nonlinear optimization techniques based on simplified dynamics models, requiring hand-tuned reference trajectories. In this article, we propose to integrate safety constraints into constrained task-space imitation learning, endowing a humanoid robot with adaptive walking capability. Specifically, unlike previous work using nonlinear and coupled capturability dynamics, we first linearize the 3-D capture conditions using appropriate extreme values and then seamlessly incorporate them into constrained imitation learning. Furthermore, we propose novel heuristic rules to define control points, enabling adaptive locomotion learning. The resulting framework allows robots to learn locomotion skills from a few demonstrations efficiently and apply the learned skills to unseen 3-D scenarios while satisfying the constraints for passive safety. Unlike deep enforcement learning, our framework avoids the need of a large number of iterations or sim-to-real transfer. By virtue of the task-space adaptability, the proposed imitation learning framework can reuse collected demonstrations in a new robot platform. We validate our method by hardware experiments on Walker2 robot and simulations on COMAN robot.

3-D walking
bipedal locomotion
constrained imitation learning
humanoid robot
Legged locomotion
Lips
passive safety
Robots
Safety
Solid modeling
Task analysis
Trajectory

http://resolver.tudelft.nl/uuid:528e1625-efd1-4d40-a802-4a9c704d1880

https://doi.org/10.1109/TMECH.2023.3239099

2023-08-20

1083-4435

IEEE - ASME Transactions on Mechatronics, 28 (6), 3029-3040

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2023 J. Ding, Tin Lun Lam, Ligang Ge, Jianxin Pang, Yanlong Huang