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ManiSkill
Large-scale manipulation benchmark and training env with GPU parallelism and diverse tasks.
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dm_control
DeepMind control suite and MuJoCo wrappers — standard continuous-control benchmarks and locomotion tasks.
Why: Canonical continuous-control / locomotion env suite
Overview
dm_control is catalogued on Embodied AI Hub as a curated project for embodied AI builders.
DeepMind control suite and MuJoCo wrappers — standard continuous-control benchmarks and locomotion tasks.
Why it matters: Canonical continuous-control / locomotion env suite
Use the official repository and docs as the source of truth for install pins, licenses, and hardware requirements.
Who it is for
Researchers, students, and builders comparing open embodied stacks.
Key highlights
- DeepMind control suite and MuJoCo wrappers — standard continuous-control benchmarks and locomotion tasks.
- Type: project; categories: simulation, framework
- Marked recommended on the hub
When to use
- You are mapping the open embodied AI landscape
- You need a starting URL and judgment note before deep-diving
When not to use
- You need production SLAs or certified industrial support only
- License or hardware constraints are incompatible with your deployment
Getting started
- 1Open the official URL and read README install pins.
- 2Skim license and citation requirements.
- 3Run the smallest official example before scaling experiments.
- 4Cross-link related hub resources (sim / data / policy) for a full stack.
Papers & reading
Caveats & pitfalls
- Hub cards are curated summaries — always verify upstream docs.
- Stars and dates drift; treat extra.stars as approximate.
Content reviewed 2026-07-28