Published January 25, 2024
| Version v2023
Dataset
Open
METRICS/ADAPT Dataset: Sim2Real Object Detection and Pose Estimation
Authors/Creators
-
1.
Laboratoire d'Intégration des Systèmes et des Technologies
Description
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.