Farama-Foundation/Metaworld resource

Health v2 · maintenance only

Adoption not part of the score

1871 stars · 351 forks observed · 2026-08-28

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

Meta-World is an open-source benchmark of 50 simulated robotic manipulation tasks built on MuJoCo and the Gymnasium API, for evaluating multi-task and meta reinforcement learning algorithms. It provides standardized benchmark suites (MT1/MT10/MT50 and ML1/ML10/ML45) with train/test task splits for few-shot adaptation research.

Use cases

• benchmark meta reinforcement learning algorithms
• evaluate multi-task RL policies on robotic manipulation
• test few-shot adaptation to new tasks
• compare RL algorithms on standardized continuous control tasks
• train policies across 50 manipulation tasks
• reproduce results from the Meta-World paper

When to choose

• you need a standardized benchmark for multi-task or meta-RL research
• you want Gymnasium-compatible robotic manipulation environments
• you need train/test task splits for few-shot adaptation experiments

When to avoid

• you need real robot hardware experiments
• you only need a single-task RL environment without benchmark structure
• you require official Windows support

Facets

dataset · maturity active

simulation machine-learning benchmarking testing reinforcement-learning robotics machine-learning simulation python meta-rl multi-task-learning mujoco gymnasium robotic-manipulation benchmark-environments linux macos

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markdown · JSON · MCP: product_card(name="Farama-Foundation/Metaworld")