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Robosuite

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Robosuite is a modular simulation framework for robot learning developed by the ARISE Initiative at Stanford University. Built on MuJoCo physics, robosuite provides a standardized suite of manipulation tasks with a clean, modular API that enables researchers to compose robots, controllers, camera configurations, and task objects. The framework supports 8 robot models (Franka Panda, Sawyer, UR5e, IIWA, Jaco, Kinova3, Fetch, and humanoids) and includes 50+ manipulation tasks ranging from simple reaching and lifting to complex assembly and nut-and-screw insertion. Each task includes multiple difficulty levels and domain-randomization parameters. Robosuite integrates tightly with robomimic for imitation learning workflows, providing demonstrations, reward functions, and standardized evaluation protocols. It is widely used in both academia and industry as a benchmark for manipulation learning research.

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Updated:6/25/2026
stars3200
languagePython

Tags

simulationMuJoComanipulationStanfordARISEbenchmarkmodularopen-source

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Sources

https://robosuite.ai/
website
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https://arxiv.org/abs/2009.12293
paper
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https://github.com/ARISE-Initiative/robosuite
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