Data & Benchmarks

Simulation Benchmark

A simulation benchmark is a standardized suite of simulated environments, tasks, and evaluation protocols used to compare robot learning methods reproducibly. Prominent examples include Meta-World and RLBench for manipulation, LIBERO for lifelong and language-conditioned learning, ManiSkill and Isaac Lab for GPU-parallel evaluation, and Habitat for embodied navigation. Benchmarks fix observation spaces, success criteria, and train-test splits so reported numbers are comparable across papers.

Why it matters for physical AI

Reproducible comparison is chronically hard on physical robots, so simulated benchmarks carry most of the field's empirical burden — while their gap to real-world difficulty must be kept in mind.