Rapid stability margin estimation for contact-rich locomotion

Conference item

The efficient evaluation the dynamic stability of legged robots on non-coplanar terrains is important when developing motion planning and control policies. The inference time of this measure has a strong influence on how fast a robot can react to unexpected events, plan its future footsteps or its body trajectory. Existing approaches suitable for real-time decision making are either limited to flat ground or to quasi-static locomotion. Furthermore, joint-space feasibility constraints are usually not considered in receding-horizon planning as their high dimensionality prohibits this. In this paper we propose the usage of a stability criterion for dynamic locomotion on rough terrain based on the Feasible Region (FR) and the Instantaneous Capture Point (ICP) and we leverage a Neural Network (NN) to quickly estimate it. We show that our network achieves satisfactory accuracy with respect to its analytical counterpart with a speed up of three orders-of-magnitude. It also enables the evaluation of the stability margin's gradient. We demonstrate this learned stability margin in two diverse applications - Reinforcement Learning (RL) and nonlinear Trajectory Optimization (TO) for legged robots. We demonstrate on a full-sized quadruped robot that the network enables the computation of physically-realizable Center of Mass (CoM) trajectories and foothold locations satisfying friction constraints and joint-torque limits in a receding-horizon fashion and on non-coplanar terrains.

Authors: Orsolino, R; Gangapurwala, S; Melon, O; Geisert, M; Havoutis, I

• Publication status: Published
• Peer review status: Reviewed (other)
• Publisher: IEEE
• Journal: Proceedings of the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• Pages: 8485-8492
• Publication date: 2021-12-16
• Event title: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• Event start date: 2021-09-27
• Event end date: 2021-10-01
• DOI: 10.1109/iros51168.2021.9636474
• Language: English
• Keywords: FFR