Benchmarks have historically defined eras of AI progress — ImageNet for vision, GLUE for language.
Physical AI is at its own inflection point, but the field has not yet agreed on what rigorous evaluation looks
like for embodied tasks. Stanford's research — in robot learning, dexterous manipulation, and benchmark
design — sits at the centre of that conversation.
Centific's CAIR Labs brings the operational dimension: live data collection across 10 egocentric domains,
120+ task definitions, and ~8,000 target demonstrations, all QA-gated and RLDS/LeRobot-ready. This is a
conversation between a researcher who defines standards and a practitioner who has to live inside them.