Using Model-Based Optimization and Motion Learning

The research proposed in this chair aims to generate complex motions for robots in real time by:

  1. relying on massive off-line caching of pre-computed optimal motions that are
  2. recovered and adapted online to new situations with real-time tractable model predictive control and where
  3. all available sensor modalities are exploited for feedback control going beyond the mere state of the robot for more robust behaviors.

The goal is to develop a unified yet tractable approach to motion generation for complex robots with arms and legs.

Programme : IA collaborative
Thèmes : Neuroscience et IA, Robotique et IA

Porteur :
Nicolas Mansard, DR CNRS, Laas


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