Object detection and pose estimation are fundamental steps for industrial robotic systems. Training algorithms with CAD models is therefore essential for the agility of industrial systems. The challenge proposed by Sim2Real Challenge is to evaluate the performance of detection, classification and pose estimation algorithms while having limited access to real data during the training phase.
Participants will have to rely on CAD models of the parts to develop and train their algorithms, while the test phase will be carried out using real images.
This challenge is a step in the ADAPT (ADvanced Agile ProducTion) competition, which aims to implement dexterous manipulation of industrial components, while facilitating the operator’s task via intuitive and multimodal interfaces. The challenge is to validate the performance and maturity of machine learning and robotics techniques, in order to integrate them into the assembly processes of industrial systems.
|1||Training and Validation Phase||28/10/2021 – 25/01/2022|
|2||Test Phase||26/01/2022 – 08/02/2022|
|3||Announcing of Results||10-20/02/2022|
More information: https://metricsproject.eu/agile-production/
The H2020 METRICS project (2020-2024), dedicated to metrological evaluation and testing campaigns of robotic and artificial intelligence systems (in the field and in cascade), is structured in 4 thematic challenges: Health (HEART-MET), Inspection and Maintenance (RAMI), Agriculture and Agri-Food (ACRE), Agile Production (ADAPT).