Towards Autonomous Data Annotation and System-Agnostic Robotic Grasping Benchmarking with 3D-Printed Fixtures

The interaction of robots with their environment requires robust object-centric perception capabilities, typically achieved using learning-based methods trained on synthetic data. However, real-world deployment demands evaluating these capabilities in relevant environments, often involving extensive manual annotation for a quantitative analysis. Additionally, standardized evaluations for robotic tasks, such as grasping, need reproducible object scene configurations and performance benchmarks. We propose a solution to both problems by temporarily employing 3D-printed components, socalled fixtures, which can be designed for any rigid object. Once the scene is set up and object poses are extracted, the fixtures are removed, leaving the natural scene without any artificial distractions. The presented approach is seemingly applicable for pre-determined configurations of multiple objects, which enables precise re-building of scenes with consistent object-toobject relations. Our suggested annotation procedure achieves strong pose accuracy solely on RGB images without any manual involvement. We evaluate and show the usability of the proposed fixtures for automated real-world data annotation to fine-tune a detector and for benchmarking object pose estimation algorithms for robotic grasping. Code and fixture meshes for 3D printing are available at https://github.com/DLRRM/fixture_generation.