SCENEREPLICA: Benchmarking Real-World Robot Manipulation by Creating Replicable Scenes
This benchmark addresses the need for standardized evaluation in robotics, enabling researchers to compare techniques more easily, though it is incremental as it builds on existing datasets and methods.
The authors tackled the problem of evaluating robot manipulation in the real world by creating a reproducible benchmark for pick-and-place tasks, using YCB objects to ensure comparability and providing experimental results for model-based and model-free 6D robotic grasping.
We present a new reproducible benchmark for evaluating robot manipulation in the real world, specifically focusing on pick-and-place. Our benchmark uses the YCB objects, a commonly used dataset in the robotics community, to ensure that our results are comparable to other studies. Additionally, the benchmark is designed to be easily reproducible in the real world, making it accessible to researchers and practitioners. We also provide our experimental results and analyzes for model-based and model-free 6D robotic grasping on the benchmark, where representative algorithms are evaluated for object perception, grasping planning, and motion planning. We believe that our benchmark will be a valuable tool for advancing the field of robot manipulation. By providing a standardized evaluation framework, researchers can more easily compare different techniques and algorithms, leading to faster progress in developing robot manipulation methods.