Gym-Anything for Robotics: Scaling physical simulations with digital agents

Key points

The authors decomposed occupations and everyday activities from U.S. GDP data into atomic physical actions, selected 120 action groups, and had coding agents build 25 of those simulations.
A creation agent and four audit agents ran a creation-audit loop for three rounds to verify simulation quality by catching physics errors and exploits.
The resulting RoboWorld covers 25 simulations with 25 test environments and 175 training RL environments, supporting swappable robots and scene variations.
All three frontier models scored 0% success on the 25 test tasks with a best average reward of 0.06, while Claude Opus 5.5 achieved a 32% success rate on an easier subset of 25 tasks.
A code policy reading privileged state succeeded on 96.7% of 2,048 varied initial states, and π0.5 trained on 1,000 of its demonstrations rose from 0% to 53% success on two tasks and 55% on unseen variations.
As a limitation, success drops as the robot's degrees of freedom increase, and the two GPT models earned no reward at all on humanoid tasks.

External research summaries. These are not HDATF publications or measured product results.