arXiv:2605.21458v1 Announce Type: cross Abstract: Suppose a planner has a pre-trained simulator of a sequential decision problem and the option to run real experiments in the field. The simulator is cheap to query but inherits confounding and drift from its calibration data. Experimentation is unbiased but consumes one real unit per trial. We study when, and how, the planner should supplement the simulator with experiments. We give three results. First, an extended simulation lemma decomposes the simulator's value error into a calibration--deployment shift that randomization can identify and a
The increasing complexity and scale of AI models and robotic systems necessitate more efficient and reliable ways to bridge the gap between simulation and real-world deployment, especially with rising compute costs and safety concerns.
This research provides a framework for optimally combining cheap, flawed simulators with expensive, unbiased real-world experiments, which is critical for the robust and cost-effective development and deployment of AI in physical systems.
The methodology for training and validating autonomous systems, particularly in robotics and other physical AI applications, becomes more scientifically rigorous and resource-efficient, potentially accelerating reliable deployment.
- · AI developers
- · Robotics companies
- · Logistics and manufacturing
- · Academic researchers
- · Companies relying purely on unvalidated simulations
- · Brute-force simulation approaches
More reliable and safer deployment of AI agents and robotic systems in real-world environments.
Reduced development costs and faster iteration cycles for hardware-integrated AI, enhancing competitive advantage.
Accelerated commercialization and broader adoption of advanced robotics and autonomous decision-making systems across industries.
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Read at arXiv cs.LG