Data & Benchmarks

Meta-World

Meta-World is a simulated benchmark, introduced by Yu et al. in 2019, comprising 50 distinct tabletop manipulation tasks performed by a Sawyer arm in MuJoCo, designed to evaluate multi-task and meta-reinforcement learning. Its standardized evaluation protocols, including MT10, MT50, ML10, and ML45, measure both simultaneous multi-task mastery and few-shot adaptation to held-out tasks with shared observation and action spaces.

Why it matters for physical AI

Shared-embodiment task suites isolate the algorithmic question of knowledge transfer across skills, providing the standard evidence base for claims about multi-task and meta-reinforcement learning methods before hardware trials.