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RLBench is a large-scale robot learning benchmark and environment for vision-guided manipulation research.

Documentation in progress

The product identity and source are confirmed, but some specifications or interface details do not yet have citable manufacturer evidence. Missing evidence is not treated as a negative claim.

Product information

Information below comes from the product website or public documentation. Pricing and availability may change.

Category
Robot Development
Maturityi
Available
Deploymenti
Self-hosted
Buying / access
Buy or sign up directly
Source
Official websiteChecked Sep 18, 2026

Reality check

A demo shows what may be possible. These points help judge whether the product fits day-to-day use.

Works well when
  • Simulation, control, perception and repeatable experiments
  • Teams that can manage dependencies and hardware interfaces
  • Prototyping before committing to a physical deployment
What to check
  • Supported hardware, GPU requirements and version compatibility
  • Documentation, examples and community response
  • How simulation results transfer to the real robot
Not a magic solution
  • 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

Product details

Manufacturer / team
Stephen James
Product typei
Framework
Open source

The cost iceberg

The purchase price is only one part of an automation project.

GPU, simulation assets and hardware adapters
Dependencies, datasets and test infrastructure
Version pinning, documentation and support
Sim-to-real gaps and safety validation