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^[1] 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.

The most revealing question is the difference between repeating a learned example and behaving well in a genuinely new situation. Generalization^[2] means coping with conditions that were not identical to the training examples. One striking video cannot tell us how many attempts failed. It helps to ask what the held-out tests looked like, whether objects and lighting changed, who selected the demonstrations and which baselines were compared. Those questions turn a fascinating result into a testable claim rather than a promise of a robot that can already do everything. Contact, friction and task conditions should be checked against real observations before relying on simulated scores.