METRICS/ADAPT Dataset: Sim2Real Object Detection and Pose Estimation

The ADvanced Agile ProducTion (ADAPT) competition, a part of the EU Horizon-2020 funded

project METRICS, is designed to address the challenges in dexterous manipulation of mechanical

parts within the assembly processes. Central to this competition are object detection and pose

estimation capabilities, which are pivotal in contemporary robotic manipulation systems and

which increasingly rely on machine learning algorithms. Hence, evaluating the performance of

these algorithms, particularly when their training is constrained by limited access to

real-world data, is crucial in assessing the readiness for deployment of such systems in

practical settings.

The ADAPT dataset was therefore created specifically for this purpose. It contains detailed

CAD models of three different assembly parts and a collection of real, annotated data. This

data is essential for testing how well part detectors and pose estimators perform in real

situations. Algorithms can be trained using synthetic images created from these CAD models,

thereby helping them learn in simulated conditions. This method is key in closing the gap

between training in simulations and working in actual environments, underscoring the dataset's

role in advancing the use of robots in industrial settings.