Safe Guaranteed Exploration for Non-linear Systems
<|reference_start|>Safe Guaranteed Exploration for Non-linear Systems: Safely exploring environments with a-priori unknown constraints is a fundamental challenge that restricts the autonomy of robots. While safety is paramount, guarantees on sufficient exploration are also crucial for ensuring autonomous task completion. To address these challenges, we propose a novel safe guaranteed exploration framework using optimal control, which achieves first-of-its-kind results: guaranteed exploration for non-linear systems with finite time sample complexity bounds, while being provably safe with arbitrarily high probability. The framework is general and applicable to many real-world scenarios with complex non-linear dynamics and unknown domains. Based on this framework we propose an efficient algorithm, SageMPC, SAfe Guaranteed Exploration using Model Predictive Control. SageMPC improves efficiency by incorporating three techniques: i) exploiting a Lipschitz bound, ii) goal-directed exploration, and iii) receding horizon style re-planning, all while maintaining the desired sample complexity, safety and exploration guarantees of the framework. Lastly, we demonstrate safe efficient exploration in challenging unknown environments using SageMPC with a car model.<|reference_end|>
@article{prajapat2024safe,
    title={Safe Guaranteed Exploration for Non-linear Systems},
    author={Manish Prajapat, Johannes K"ohler, Matteo Turchetta, Andreas Krause,
  Melanie N. Zeilinger},
    journal={arXiv preprint arXiv:2402.06562},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.06562},
    primaryClass={eess.SY cs.LG cs.RO cs.SY math.OC}
}