Towards Building AI-CPS with NVIDIA Isaac Sim: An Industrial Benchmark and Case Study for Robotics Manipulation

Simulation is critical for safe AI-robotics development: The paper establishes that a reliable simulation platform (NVIDIA Isaac Sim) is essential for testing AI-controlled robots before deployment, addressing the "inherent challenge of explaining AI components" that introduces uncertainty and unreliability in real-world systems.

Standardized benchmarks enable accountability: The authors propose a public industrial benchmark with eight representative manipulation tasks and multiple AI controllers, allowing researchers and developers to systematically evaluate and compare AI performance—a necessary step for responsible deployment of AI in physical systems.

Testing frameworks must bridge simulation and reality: The paper develops a "falsification framework" that works across both physics simulations and real environments, recognizing that AI systems tested only in simulation may fail unpredictably in the physical world, a key concern for responsible AI-CPS development.

Multiple AI controllers should be evaluated systematically: By testing various AI software controllers on the same tasks, the benchmark enables transparent comparison of different approaches, helping stakeholders understand which methods are more reliable and suitable for specific industrial applications.

Explainability remains a foundational challenge: The paper explicitly identifies that AI components in robotic systems lack transparency ("inherent challenge of explaining AI components"), suggesting that responsible deployment requires not just performance testing but also ongoing work to make AI decision-making more interpretable to operators and safety engineers.