Using model-based optimization and movement learning

Chair objectives

How to generate complex movements for arbitrary robots with arms and legs physically interacting in a dynamic environment?

This chair investigates artificial movement and biomechanics using advanced numerical methods and reinforcement learning (RL), while targeting effective movements on real robot platforms.

We recently demonstrated the premiere whole-body predictive control on the full-scale humanoid robot Talos using a pre-trained memory of motion.

The objective of the chair is to investigate the foundation of polyarticulated movement by combining advanced numerical methods (optimal control, constrained second-order optimization)and data-driven.

Program Collaborative AI
Themes: Neuroscience and AI, Robotics and AI

Chair holder:
Nicolas Mansard, DR CNRS, Laas


  • Olivier Stasse (LAAS CNRS)
  • Olivier Cots (Toulouse INP, IRIT)

Website :

Know more

Running a robot: a permanent challenge

Will we one day be able to exclaim "what an elegant step!" "At the sight of a moving humanoid robot? The roboticist Nicolas Mansard strives to perfect the movements of robots using artificial intelligence ... without seeking to reproduce the human walk identically.

Read the article in French

Ne manquez rien !

Inscrivez-vous pour recevoir l'actualité d'ANITI chaque mois.

Nous n’envoyons pas de messages indésirables !