Provably Safe Sim-to-Real Transfer
A paper on arXiv proposes a reward-free safe reinforcement learning algorithm for sim-to-real transfer that provably reduces real-world interaction while ensuring safe exploration and enabling near-optimal feasible policies.
The paper addresses safe sim-to-real transfer, where an agent uses an imperfect simulator to reduce real-world sample complexity while maintaining safety constraints during data collection. It formulates the problem within reward-free safe RL and presents a computationally efficient algorithm with provable guarantees.
The algorithm leverages simulator information to reduce real-world interaction, likely through uncertainty quantification or domain adaptation, while enforcing safety constraints during exploration. The reward-free formulation suggests learning a policy that can be adapted to any reward function without additional unsafe exploration.
This research could enable safer deployment of RL in robotics and healthcare, where real-world data collection is expensive and safety-critical. It may lower barriers to sim-to-real transfer by providing formal safety guarantees.
Provably safe sim-to-real transfer could reduce development costs and time-to-market for autonomous systems by minimizing real-world testing while ensuring safety compliance.
Next signals include empirical validation on robotic benchmarks, extensions to partial observability or multi-task settings, and potential integration with existing safe RL libraries.