Data & Benchmarks

RLBench

RLBench is a large-scale benchmark and learning environment for robot manipulation, introduced by James et al. in 2020, providing 100 language-described tasks of graded difficulty executed by a simulated Franka Panda arm in CoppeliaSim. Each task supplies procedurally varied scenes and an unlimited stream of scripted expert demonstrations, supporting imitation learning, reinforcement learning, and few-shot evaluation, and it became the standard testbed for language-conditioned agents such as PerAct.

Why it matters for physical AI

Shared simulated benchmarks with demonstrations let manipulation methods be compared on equal footing, and RLBench's language-conditioned multi-task design anticipated how robot foundation models are now evaluated.