Should simulation benchmarks for robotics focus on true generalization testing

Your demand for "true generalization" ignores the fundamental data-driven reality of the sim-to-real gap: current simulators cannot perfectly model complex physics like tactile feedback, micro-friction, or soft-body dynamics. Consequently, forcing benchmarks to test broad generalization across novel objects and physical perturbations merely trains agents to overfit to the simulator's specific inaccuracies, resulting in overly conservative policies that still fail in reality. Instead of chasing the illusion of general intelligence in flawed virtual environments, simulation benchmarks must mathematically prioritize strict dynamic fidelity and the absolute mastery of narrow, primitive skills. Furthermore, the commercial robotics industry—from manufacturing to logistics—demands 99.99% reliability in structured environments, making the rigorous optimization of specific tasks far more valuable than abstract adaptability. We must build benchmarks that act as proving grounds for precise, task-specific physics and hardware-software co-design, rather than scoreboards for a generalized adaptability that simulators cannot authentically measure.

Relying on broad simulated variations—essentially domain randomization—to bridge the sim-to-real gap is a documented dead end that mathematically forces policies to become overly conservative and incapable of executing precision tasks. Your logistics example ignores modern industrial reality: automated warehouses achieve 99.99% uptime not through zero-shot algorithmic generalization, but through strict environmental structuring, specialized compliant hardware, and exact system identification. When benchmarks prioritize infinite "unexpected" variations, they dilute the optimization signal, preventing models from mastering the complex, non-linear dynamics required for advanced, contact-rich manipulation. Empirical data consistently demonstrates that highly tuned, task-specific policies trained in high-fidelity, meticulously calibrated simulations significantly outperform generic, "robust" models when deployed on physical hardware. Therefore, simulation benchmarks must remain rigorous engineering tools focused on exact physical alignment and specialized skill verification, rather than sacrificing peak performance to chase the algorithmic fantasy of unbounded adaptability.

Your attempt to test anthropomorphic concepts like "physical reasoning" or "object permanence" in simulation fundamentally misunderstands the technology: simulators integrate differential equations, they do not evaluate cognitive understanding. When physical variables like gripper wear or texture friction inevitably shift in reality, the proven engineering solution is real-time system identification and adaptive control loops, not relying on a generic baseline hallucinated from artificially randomized simulated environments. Ultimately, prioritizing "true generalization" in benchmarks is a misguided pursuit of AGI that forces models to overfit to a simulator's inherent physics approximations, resulting in universally mediocre, overly conservative policies. Simulation must instead remain a rigorous tool for what it mathematically excels at: high-fidelity system verification, precise task optimization, and exact sim-to-real parameter alignment. By rejecting the illusion of unbounded virtual adaptability in favor of mastering specialized, measurable physical dynamics, we engineer the highly reliable, precise robotics systems that actually scale in the real world.