SCENEREPLICA: Benchmarking Real-World Robot Manipulation by Creating Replicable Scenes

Autor:	Khargonkar, Ninad, Allu, Sai Haneesh, Lu, Yangxiao, P, Jishnu Jaykumar, Prabhakaran, Balakrishnan, Xiang, Yu
Rok vydání:	2023
Předmět:	Computer Science - Robotics Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted to ICRA 2024. Project page is available at https://irvlutd.github.io/SceneReplica
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2306.15620 Zobrazit plný text záznamu View this record from Arxiv