Constructing a realistic virtual training environment can require extensive expert labor before a robot starts learning. Pranjal Aggarwal and Sean Welleck propose Gym-Anything, where coding agents build simulations and an adversarial audit checks whether their physics and tasks satisfy explicit criteria. The selection of physical work draws on US economic activity instead of focusing solely on familiar toy demonstrations.

RoboWorld contains 25 simulations, 175 training environments and 25 held-out test environments. According to the authors, even the strongest evaluated frontier models failed every test task, averaging 0.06 reward. Reward is a numerical simulator score, not a real-world robot success percentage. The paper also explores learning from repeated attempts, privileged teachers that can inspect simulator state, and demonstrations produced by code policies.

Several approaches improved the measured results, but transfer to physical hardware remains unproven. Automatically generated worlds can misrepresent friction, contact dynamics and unexpected events in actual industrial settings.

For a customer, the central question is how the approach fits an existing workflow without hidden costs. Integration^[1] includes sensors, control interfaces, staff training, logs and a procedure for human intervention. A pilot should define acceptable risk, cost per successful operation and the consequences of downtime in advance. A laboratory score alone cannot support a credible return-on-investment calculation. A supplier should demonstrate the system on the customer's own objects and data and explain how it will be maintained after deployment. Contact, friction and task conditions should be checked against real observations before relying on simulated scores.