robosuite

Modular MuJoCo simulation framework for robot learning research

License

MIT

Overview

robosuite is a modular simulation framework for robot manipulation research, built on the MuJoCo physics engine. It provides standardized environments, robot models such as Franka Panda, UR5 and Sawyer, and benchmark tasks for reproducible research. Robots, grippers, objects and tasks can be mixed and matched, and teleoperation interfaces support collecting human demonstrations.

RoboSuite is a modular simulation framework for robot manipulation research built on the MuJoCo physics engine, providing standardized environments and benchmarks for reproducible manipulation research. The framework offers extensive libraries of robot models including popular research platforms like Franka Panda, UR5, and Sawyer, along with composable environment components for constructing custom manipulation scenarios. Standardized task definitions and evaluation metrics enable direct comparison of different algorithms and approaches across research groups. The modular architecture allows researchers to easily swap robot models, customize environments, and define new tasks while maintaining consistency in simulation fidelity and evaluation protocols. By emphasizing standardization and reproducibility, RoboSuite addresses the challenge of comparing manipulation algorithms across different codebases and experimental setups.

Why it matters

robosuite is one of the most widely-used simulation frameworks for robot manipulation research. Its modular design and standardized benchmarks have been cited in hundreds of papers, making it a common baseline for comparing manipulation algorithms.

Problem it solves

Robot manipulation research requires expensive hardware and is difficult to reproduce across labs. robosuite provides a standardized simulation environment where researchers can develop and benchmark algorithms without physical robots.

Key takeaways

• Modular design lets you mix and match robots, grippers, objects, and tasks
• Built on MuJoCo physics engine for fast, accurate contact simulation
• Includes standardized benchmark tasks used in hundreds of published papers
• Supports both single-arm and dual-arm manipulation scenarios
• Provides teleoperation interfaces for human demonstration collection

Who it is for

• Robotics researchers benchmarking manipulation algorithms
• ML engineers developing imitation learning pipelines
• Students learning robot control and manipulation
• Teams collecting simulated demonstrations for policy training

Limitations

• Sim-to-real transfer still requires domain adaptation
• MuJoCo contact dynamics may not perfectly match real-world deformable objects
• Limited to tabletop manipulation scenarios out of the box