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Whole-body MPC and sensitivity analysis of a real time foot step sequencer for a biped robot Bolt

Published in IEEE-ICHR, 2024

This paper introduces a novel whole-body model predictive controller combined with a footstep sequencer for the bipedal robot Bolt, enabling robust locomotion. Simulations show effective velocity tracking and recovery from pushes and slips. Additionally, a theoretical sensitivity analysis of the footstep sequencing problem is provided to deepen result insights.

Recommended citation: Constant Roux, Côme Perrot, Olivier Stasse. Whole-body MPC and sensitivity analysis of a real time foot step sequencer for a biped robot Bolt, IEEE-ICHR 2024.
arxiv

Reinforcement Learning from Wild Animal Videos

Published in arxiv, 2024

We present RLWAV, a method to teach legged robots locomotion skills by learning from thousands of wild animal videos. Using a video classifier and reinforcement learning, our approach enables a robot to acquire diverse behaviors like walking and jumping in simulation and transfers these skills directly to a real quadruped, despite the domain and embodiment gap.

Recommended citation: Elliot Chane-Sane, Constant Roux, Olivier Stasse, Nicolas Mansard. Reinforcement Learning from Wild Animal Videos.
arxiv | Project Page

Constrained Reinforcement Learning for Unstable Point-Feet Bipedal Locomotion Applied to the Bolt Robot

Published in IEEE-ICHR, 2025

In this paper, 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.

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.
arxiv | Project Page