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ARISE-Initiative/robosuite

robosuite: A Modular Simulation Framework and Benchmark for Robot Learning observed · 2026-08-28

github.com/ARISE-Initiative/robosuite · homepage · Python · NOASSERTION (other) observed · 2026-08-28

Health v2 · maintenance only

72/100

  • Activity 92
  • Release rhythm 30
  • Longevity 100

Flags: no_license

How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.

  • gap_med: 210.0
  • age_days: 2869
  • days_rel: 252
  • days_push: 53
  • n_releases_24m: 3

Full methodology

Adoption not part of the score

2581 stars · 760 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

robosuite is a modular simulation framework powered by the MuJoCo physics engine for robot learning, offering standardized benchmark environments for reproducible research. It supports diverse robot embodiments including humanoids, custom robot composition, composite controllers, teleoperation devices, and photo-realistic rendering.

Use cases

  • train reinforcement learning agents for robot manipulation tasks
  • benchmark imitation learning algorithms on standardized robot tasks
  • simulate humanoid and multi-robot embodiments in MuJoCo
  • design custom robot simulation environments with modular components
  • collect teleoperated demonstration data for robot learning
  • run reproducible robotics research without physical hardware
  • apply domain randomization and dynamics randomization for sim-to-real transfer

When to choose

  • you need standardized benchmark environments for robot manipulation research
  • you want to train RL or imitation learning policies in simulation before deploying to hardware
  • you need support for diverse robot embodiments including humanoids
  • you require reproducible, community-adopted simulation for embodied AI research

When to avoid

  • you need a physics engine itself rather than a task framework built on one
  • your focus is mobile robot navigation or locomotion rather than manipulation
  • you need photorealistic simulation beyond what MuJoCo rendering provides
  • you require a non-Python or real-time hardware control stack

Facets

framework · maturity active

simulation machine-learning reinforcement-learning benchmarking robotics robotics reinforcement-learning simulation machine-learning python windows mujoco robot-learning robot-manipulation imitation-learning benchmark-environments sim-to-real embodied-ai research linux macos

5 sources

Member repositories

RepositoryRoleHealth v2
ARISE-Initiative/robosuitemain72

For agents

markdown · JSON · MCP: product_card(name="ARISE-Initiative/robosuite")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem