Provably Safe Sim-to-Real Transfer

• URL: http://arxiv.org/abs/2609.01418v1
• Date: Tue, 01 Sep 2026 15:34:57 GMT
• Title: Provably Safe Sim-to-Real Transfer
• Abstract summary: A common practice is to first train a policy in a simulator, where samples are cheap, and then deploy the learned policy in the real world.<n>Such direct sim-to-real transfer is not guaranteed to succeed due to sim-to-real mismatch.<n>We design a computationally efficient algorithm that exploits simulator information to provably reduce real-world interaction.
• Score: 8.354731976915588
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: To mitigate the sample complexity of real-world reinforcement learning (RL), a common practice is to first train a policy in a simulator, where samples are cheap, and then deploy the learned policy in the real world with the hope that it generalizes effectively. Such direct sim-to-real transfer is not guaranteed to succeed: simulator-trained policies can be suboptimal in the real world due to sim-to-real mismatch. Correcting this mismatch requires collecting data from the real system, but in many applications, such as robotics and healthcare, this data-collection process is itself subject to safety constraints. This gives rise to the problem of safe sim-to-real transfer: how can an agent exploit an imperfect simulator while ensuring safe real-world data collection and learning a near-optimal feasible policy for the target system? We address this problem by formulating safe sim-to-real transfer within the framework of reward-free safe RL. We design a computationally efficient algorithm that exploits simulator information to provably reduce real-world interaction while ensuring safe exploration and enabling the computation of a near-optimal feasible policy for any potential reward function. Our real-world sample complexity bound characterizes the benefit of using the simulator in terms of the sim-to-real mismatch.

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