OCRTOC: A Cloud-Based Competition and Benchmark for Robotic Grasping and Manipulation
Ziyuan Liu, Wei Liu, Yuzhe Qin, Fanbo Xiang, Minghao Gou, Songyan Xin,, Maximo A. Roa, Berk Calli, Hao Su, Yu Sun, Ping Tan

TL;DR
OCRTOC is a cloud-based benchmark and competition platform for robotic grasping and manipulation, enabling standardized, reproducible experiments on real robots and simulations to advance research in object rearrangement tasks.
Contribution
This work introduces a novel cloud-based benchmark and competition framework for robotic manipulation, providing standardized real robot setups and simulation tools to facilitate fair performance comparisons.
Findings
59 teams participated in the 2020 IROS competition
Standardized scenarios enable fair algorithm performance evaluation
The platform lowers barriers for reproducible robotic research
Abstract
In this paper, we propose a cloud-based benchmark for robotic grasping and manipulation, called the OCRTOC benchmark. The benchmark focuses on the object rearrangement problem, specifically table organization tasks. We provide a set of identical real robot setups and facilitate remote experiments of standardized table organization scenarios in varying difficulties. In this workflow, users upload their solutions to our remote server and their code is executed on the real robot setups and scored automatically. After each execution, the OCRTOC team resets the experimental setup manually. We also provide a simulation environment that researchers can use to develop and test their solutions. With the OCRTOC benchmark, we aim to lower the barrier of conducting reproducible research on robotic grasping and manipulation and accelerate progress in this field. Executing standardized scenarios on…
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Taxonomy
TopicsRobot Manipulation and Learning · Modular Robots and Swarm Intelligence · Soft Robotics and Applications
