Meta-World — Multi-Task and Meta-Reinforcement Learning
Meta-World is a simulated multi-task and meta-reinforcement learning benchmark developed at UC Berkeley and Carnegie Mellon University. Released in 2019 under MIT license, it provides 50 distinct robotic manipulation tasks simulated with a Sawyer robot arm, covering a diverse range of tabletop manipulation operations including pushing, reaching, picking, placing, and tool use. The benchmark is designed for evaluating multi-task learning — training a single policy across all 50 tasks — and meta-learning — training policies that can rapidly adapt to new tasks. Meta-World became the standard benchmark for multi-task robot RL research, with leaderboard-tracked results enabling systematic algorithm comparison. The dataset contains over 500,000 demonstration trajectories generated by scripted policies across all task variations. Meta-World is maintained by the Farama Foundation as part of their open-source robotics benchmark suite.
| Year | 2019 |
|---|---|
| Episodes | 500,000 |
| Embodiments | Sawyer (simulated) |
| Modalities | rgb, proprioception |
| Task categories | manipulation, pick-and-place, inspection |
| Data format | json, pkl |
| License | MIT |
| Access | open — commercial use permitted |
| Maintainer | UC Berkeley, Carnegie Mellon University |
| Origin country | US |
What is it?
Meta-World is a simulated multi-task and meta-reinforcement learning benchmark developed at UC Berkeley and Carnegie Mellon University. Released in 2019 under MIT license, it provides 50 distinct robotic manipulation tasks with a simulated Sawyer robot arm covering pushing, reaching, picking, placing, and tool use. Meta-World evaluates two capabilities: multi-task learning (a single policy across all 50 tasks simultaneously) and meta-learning (policies that rapidly adapt to new tasks). Maintained by the Farama Foundation.
Who is it for?
Researchers working on multi-task robot RL, meta-learning, and generalisation. The standard benchmark for algorithms that aim to learn many tasks simultaneously — a key capability for general-purpose robot deployment.
Key specifications
- Tasks: 50 distinct manipulation tasks
- Demonstrations: 500,000+ scripted trajectories
- Robot platform: Simulated Sawyer arm
- Evaluation: MT-10, MT-50, ML-10, ML-45
- Format: JSON, pickle
- License: MIT — commercial use permitted
- Access: Open — GitHub (Farama Foundation)
How it compares
The multi-task RL benchmark. LIBERO covers fewer tasks across 4 suites with better support for imitation learning and continual learning. RoboCasa covers more household-specific tasks but focuses on sim-to-real transfer. Meta-World's 50-task diversity and leaderboard make it the reference for multi-task algorithm papers.
Limitations and access notes
Simulation-only with a Sawyer arm. Tabletop manipulation only — no locomotion or mobile manipulation. MIT license permits unrestricted commercial use.
Linked professions
Frequently asked questions
What is Meta-World?
Meta-World is a benchmark of 50 simulated robotic manipulation tasks for evaluating multi-task learning (one policy on all tasks) and meta-learning (policies that quickly adapt to new tasks). Uses a simulated Sawyer robot arm, maintained by the Farama Foundation.
What is the difference between MT-10, MT-50, ML-10, and ML-45?
MT-10 and MT-50 are multi-task benchmarks where policies are trained and evaluated on 10 or 50 tasks simultaneously. ML-10 and ML-45 are meta-learning benchmarks where policies are trained on a subset and evaluated on held-out tasks, testing rapid adaptation to novel tasks.
Can Meta-World be used commercially?
Yes. Meta-World is MIT licensed, permitting unrestricted commercial use.
How do I access Meta-World?
Meta-World is available via github.com/Farama-Foundation/Metaworld and the project website meta-world.github.io. No registration is required.
Why is Meta-World maintained by Farama Foundation?
The Farama Foundation is a non-profit maintaining open-source RL environments. Meta-World was transferred to Farama to ensure long-term maintenance and compatibility with modern RL frameworks including Gymnasium.