Memory of Motion – project Memmo aims to develop a unified yet tractable approach to motion generation for complex robots with arms and legs. We are using our biped humanoid TALOS, owned by LAAS-CNRS and created by PAL Robotics for testing tasks like electrical drilling and/or fastening hand-held device, while climbing a set of stairs in Airbus experimental production line.
A technology for generating complex movements for arbitrary robots with arms and legs interacting in a dynamic environment in real-time would certainly revolutionize the motion capabilities of robots and unlock a wide range of very concrete industrial and service applications: robots would be able to react in real-time to any change of the environment or unexpected disturbance during locomotion or manipulation tasks. However, the computation of complex movements for robots with arms and legs in multi-contact scenarios in unstructured environments is not realistically amenable to real-time with current computational capabilities and numerical algorithms.
The project Memmo aims to solve this problem by:
1) Relying on massive off-line caching of pre-computed optimal motions
2) These motions are recovered and adapted online to new situations with real-time tractable model predictive control
3) In these new situations, all available sensor modalities are exploited for feedback control going beyond the mere state of the robot for more robust behaviours.
As a result, project MEMMO will develop a unified yet tractable approach to motion generation for complex robots with arms and legs.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 780684.