Constrained Reinforcement Learning for Unstable Point-Feet Bipedal Locomotion Applied to the Bolt Robot
Published in IEEE-ICHR, 2025
Recommended citation: Constant Roux, Elliot Chane-Sane, Ludovic De Matteïs, Thomas Flayols, Jérôme Manhes, Olivier Stasse, Philippe Souères. Constrained Reinforcement Learning for Unstable Point-Feet Bipedal Locomotion Applied to the Bolt Robot, IEEE-ICHR 2025. https://arxiv.org/pdf/2503.22459
Abstract
Bipedal locomotion is a key challenge in robotics, particularly for robots like Bolt, which have a point-foot design. This study explores the control of such underactuated robots using constrained reinforcement learning, addressing their inherent instability, lack of arms, and limited foot actuation. We present a methodology that leverages Constraints-as-Terminations (CaT) and domain randomization techniques to enable sim-to-real transfer. Through a series of qualitative and quantitative experiments, we evaluate our approach in terms of balance maintenance, velocity control, and responses to slip and push disturbances. Additionally, we analyze autonomy through metrics like the cost of transport and ground reaction force. Our method advances robust control strategies for point-foot bipedal robots, offering insights into broader locomotion.
Recommended citation: Constant Roux, Elliot Chane-Sane, Ludovic De Matteïs, Thomas Flayols, Jérôme Manhes, Olivier Stasse, Philippe Souères. Constrained Reinforcement Learning for Unstable Point-Feet Bipedal Locomotion Applied to the Bolt Robot, IEEE-ICHR 2025.