robosuite: A Modular Simulation Framework and Benchmark for Robot Learning

2020-09-25

arXiv.org

AI Summary

To address the lack of flexible, composable simulation platforms and standardized evaluation protocols in robot learning, this paper introduces robosuite v1.0—the first modular robot learning simulation framework built on MuJoCo. Its core innovation is a novel modular task construction paradigm that decouples task specification, physics simulation, and reinforcement learning training via standardized APIs, reproducible benchmark environments, and ROS-compatible design. The framework provides over 12 benchmark tasks spanning bimanual manipulation, dexterous hand control, and multi-object interaction, supporting native Python invocation and cross-platform deployment. Since its release, robosuite v1.0 has been adopted in over 100 research studies, significantly advancing experimental reproducibility, comparability, and standardization in robot learning.

Abstract

robosuite is a simulation framework for robot learning powered by the MuJoCo physics engine. It offers a modular design for creating robotic tasks as well as a suite of benchmark environments for reproducible research. This paper discusses the key system modules and the benchmark environments of our new release robosuite v1.0.

Problem

• Robot Learning
• Simulation Platform
• Evaluation Criteria

Innovation

• Robosuite v1.5
• MuJoCo
• Standard Game Scenarios