The ADAPT competition
The ADvanced Agile ProducTion (ADAPT) competition aims at addressing typical dexterous manipulation tasks (e.g. heap sorting, picking of parts and precision placement) of industrial components involving intuitive, multi-modal interfaces and human communication channels (speech, gestures, gaze, etc.), involved in the assembly process of an industrial mechanical system.
The main functionalities tested in separate benchmarks (FBM) include the detection and classification of parts, estimation of part poses, and quality control of an assembly. The task-based benchmarks (TBM) address collaborative programming for assembly as well as the collaborative assembly of complex parts. Each benchmark is oriented towards industry-relevant problems. For example, parts feature reflective surfaces and unstable resting poses (e.g. cylindrical shapes).
The ADAPT competition is divided into two campaigns:
Cascade campaign – is an online competition with data and targets only the functionality benchmarks, such as object detection, pose estimation and quality control by utilizing visual sensors.
Field campaign – is a physical competition with real robots and targets task benchmarks such as collaborative robot programming and coordination.
ICRA 2023 ADAPT Human-Robot Collaborative Assembly Challenge
The ICRA 2023 METRICS ADAPT Human-Robot Collaborative Assembly Challenge will take place remotely from June 1-2, 2023. The challenge aims at addressing typical dexterous robot manipulation tasks (e.g., picking of parts and precision placement, parts insertion) of parts involving intuitive, multi-modal interfaces and human communication channels (e.g., human demonstration by hand-guiding). Selected parts include gear systems that require assembly with tight tolerances, and that can be 3D printed in participants’ premises. The tasks include object detection and pose estimation, robotic object manipulation in free space and in contact, robot motion planning, collision detection, among many others.
Sim2Real object Detection and Pose-Estimation Challenge
In this Cascade competition, using simulation data, two main benchmarks will be used to test and evaluate the competitor’s solutions for Object Detection and Classification and Pose-Estimation of typical industrial parts.
During the campaign participants were able to evaluate the performance of your robot system and algorithms, in the field of agile production, with respect to assembly and human-robot collaboration. ADAPT benchmarks evaluate robot perception and actions (e.g., object detection, pose estimation, grasping and manipulation and collaborative robot programming). Objects used are 3D printed gear assemblies to be manipulated by any robot and any gripper.
Preparations for the ADAPT competition are active in the form of dry-runs. More info available at the campaign's webpage.
CEA LIST is coordinating the Sim2Real Challenge under ADAPT Competition. In this challenge, the task was to detect and recognize different parts of a gear assembly system and to estimate their position and orientation. The goal here is to assess the performance of detection, classification and pose-estimation algorithms that have limited access to real data during the training phase.
The ADAPT evaluation plan available below describes the competition and its evaluation and will be continuously updated. Additional information will be posted here as well.
Roel Pieters (Tampere University, Finland)
Tim Claudius Stratmann (OFFIS, Germany)
Pierre Loonis (Proxinnov, France)
Farzam Ranjbaran (CEA, France)