Project
robosuite
Modular MuJoCo-based robot simulation framework for composing manipulation tasks and algorithms.
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ManiSkill
Large-scale manipulation benchmark and training env with GPU parallelism and diverse tasks.
Why: Rich manipulation tasks for policy learning and evaluation
Overview
ManiSkill is a large-scale manipulation benchmark and training environment with GPU-parallel simulation. It emphasizes diverse object interactions, realistic task definitions, and high throughput for policy learning and evaluation.
Researchers use ManiSkill when they want something between “toy MuJoCo task” and “full Isaac stack”: rich manipulation tasks, point-cloud / RGB observations, and training speed suitable for academic GPUs. Official datasets support imitation learning baselines so you can compare fairly.
It is a strong choice for papers that need multi-task manipulation results without locking the entire lab into Omniverse.
Who it is for
Manipulation policy researchers; multi-task IL/RL experimenters; teams without full Isaac Sim commitment.
Key highlights
- GPU-parallel manipulation tasks at research scale
- Diverse objects and task suites for generalization studies
- Official demos for imitation learning baselines
- Flexible observation modalities (RGB, depth, point cloud)
- Active open-source community around skill learning
When to use
- You need multi-task manipulation benchmarks with speed
- You study 3D observations (point clouds) in sim
- You want open data + env in one project family
When not to use
- Pure navigation / photoreal indoor scanning (Habitat)
- Real-robot-only pipelines with no sim component
- You already standardized solely on Isaac Lab tasks
Getting started
- 1Install ManiSkill following the official GPU requirements.
- 2Run a demo task with random actions to verify rendering.
- 3Train a simple BC or RL baseline on an official demo set.
- 4Report success rates per task family, not a single average only.
- 5Compare against published baselines before claiming SOTA.
Install / setup
See ManiSkill docs
pip install mani_skill
Papers & reading
How it compares
★ 2.5k
Dataset
Meta-World
Classic multi-task manipulation sim benchmark (50 tasks) for meta and multi-task RL.
Caveats & pitfalls
- Task definitions and versions change — cite the exact ManiSkill version.
- Sim success still needs real-world validation for deployment claims.
- Heavy scenes can still OOM; start with smaller num_envs.
Content reviewed 2026-07-23