RLBench is a large-scale robot learning benchmark and environment for vision-guided manipulation research.
Product passport
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- Category
- Robot Development
- Maturityi
- Available
- Deploymenti
- Not specified
- Purchase access
- Public purchase / signup
- Official source
- Official website
- Verifiedi
- Not yet verified
Reality check
The useful boundary is what works repeatedly in a real environment—not what looks impressive in a demo.
- Simulation, control, perception and repeatable experiments
- Teams that can manage dependencies and hardware interfaces
- Prototyping before committing to a physical deployment
- Supported hardware, GPU requirements and version compatibility
- Documentation, examples and community response
- How simulation results transfer to the real robot
- A guarantee that simulation equals a production cell
- A replacement for safety validation on hardware
- A low-maintenance toolchain without version ownership
About
RLBench is developed by Stephen James and colleagues and was published in IEEE Robotics and Automation Letters in 2020. It is a large-scale benchmark and learning environment built around CoppeliaSim v4.1.0 and PyRep, aimed at vision-guided manipulation research including reinforcement learning, imitation learning, multi-task learning, geometric computer vision, and few-shot learning. It provides task sets for few-shot and multi-task learning, Gym compatibility, demonstration collection, sim-to-real visual randomization, and swappable robot arms.
Scenes
The cost iceberg
The purchase price is only one part of an automation project.
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