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RLBench
Large-scale robot learning benchmark with 100+ tabletop tasks in V-REP/CoppeliaSim.
Why: Classic multi-task tabletop manipulation benchmark
Overview
RLBench is a curated project on Embodied AI Hub.
Large-scale robot learning benchmark with 100+ tabletop tasks in V-REP/CoppeliaSim.
Why it matters: Classic multi-task tabletop manipulation benchmark
Always verify install pins, licenses, and hardware requirements on the official site before production use.
Who it is for
Builders mapping open embodied stacks; researchers comparing SOTA baselines.
Key highlights
- Large-scale robot learning benchmark with 100+ tabletop tasks in V-REP/CoppeliaSim.
- Type: project; tags: 基准, 操作, 开源
- Hub recommended
When to use
- You need this category of open resource in your stack map
- You are shortlisting baselines before deep evaluation
When not to use
- License or hardware constraints block adoption
- You only need a closed commercial stack with vendor SLA
Getting started
- 1Open the official URL and read the README / model card.
- 2Check license and citation requirements.
- 3Run the smallest official example before scaling.
- 4Cross-link related hub resources for a full pipeline.
Papers & reading
Caveats & pitfalls
- Hub cards are curated summaries — verify upstream docs.
- Stars and release names drift; treat metadata as approximate.
Content reviewed 2026-07-28