Periodic Reporting for period 3 - MEMMO (Memory of Motion)
Período documentado: 2020-10-01 hasta 2022-06-30
1) a massive amount of pre-computed optimal motions are generated offline and compressed into a "memory of motion".
2) these trajectories are recovered during execution and adapted to new situations with real-time model predictive control, allowing generalization to dynamically changing environments.
3) available sensor modalities (vision, inertial, haptic) are exploited for feedback control which goes beyond the basic robot state with a focus on robust and adaptive behavior.
To demonstrate this approach, MEMMO is organized around 3 relevant industrial applications, where MEMMO technologies have a huge innovation potential. For each application, we will demonstrate the proposed technology in industrial or medical environments, following specifications designed by the end-users partners of the project.
1) A high-performance humanoid robot will perform advanced locomotion and industrial tooling tasks in a 1:1 scale demonstrator of an aircraft assembly.
2) An advanced exoskeleton paired with a paraplegic patient will demonstrate dynamic walking on flat floor, slopes and stairs, in a rehabilitation center under medical surveillance.
3) A challenging inspection task in a real construction site will be performed with a quadruped robot. While challenging, these demonstrators are feasible, as assessed by preliminary results obtained by MEMMO partners.
Memmo is a collaborative project that seeks a breakthrough in the use of complex robots in industrial and medical scenarios. Based on the definition of a new concept, the "memory of motion", it will lead to a new technology for robot control, that has potential to impact several market domains. The variety of impacts is emphasized by demonstrating the technology in 3 relevant environments, defined by our partner stakeholders: PAL ROBOTICS in Barcelona is targeting the market of mobile robots for end-users like AIRBUS; WANDERCRAFT in Paris has designed one of the most advanced exoskeleton for paraplegics, targeting, for the first time, rehabilitation centers like those handle by the Center for Physical Medicine and Rehabilitation of APAJH, based in Pionsat, France; and COSTAIN, a civil-engineering stakeholder, is seeking new solutions for inspection in its constructions sites. The objectives of the project require a collaborative joining expertise in motion planning (brought by LAAS-CNRS, Toulouse, France), robot learning (brought by IDIAP, Switzerland), computer vision (brought by University of Oxford, UK), force control (brought by Max-Planck Institute, Tubingen, Germany and University of Trento, Italy), optimal control (brought by University of Edinbourgh, UK), and capabilities to set up realistic pilot experiments in robotics.
The main theoretical tools have been released during the first period, in particular the optimal control software Crocoddyl and first memories of motion but also other softwares for robot estimation, localization and mapping, low-level control, new mechatronic design, etc. We have produced the datasets for several experimental scenarios, benchmarked several learning formulation to encode the memory. The second period has been used for the preliminary experimental validations. The third period has been dedicated to the final definition of the industrial and medical scenarios and the implementation of the proposed method on the final demonstrators.