{"ID":2875629,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.02808","arxiv_id":"2509.02808","title":"Improving the Resilience of Quadrotors in Underground Environments by Combining Learning-based and Safety Controllers","abstract":"Autonomously controlling quadrotors in large-scale subterranean environments is applicable to many areas such as environmental surveying, mining operations, and search and rescue. Learning-based controllers represent an appealing approach to autonomy, but are known to not generalize well to `out-of-distribution' environments not encountered during training. In this work, we train a normalizing flow-based prior over the environment, which provides a measure of how far out-of-distribution the quadrotor is at any given time. We use this measure as a runtime monitor, allowing us to switch between a learning-based controller and a safe controller when we are sufficiently out-of-distribution. Our methods are benchmarked on a point-to-point navigation task in a simulated 3D cave environment based on real-world point cloud data from the DARPA Subterranean Challenge Final Event Dataset. Our experimental results show that our combined controller simultaneously possesses the liveness of the learning-based controller (completing the task quickly) and the safety of the safety controller (avoiding collision).","short_abstract":"Autonomously controlling quadrotors in large-scale subterranean environments is applicable to many areas such as environmental surveying, mining operations, and search and rescue. Learning-based controllers represent an appealing approach to autonomy, but are known to not generalize well to `out-of-distribution' enviro...","url_abs":"https://arxiv.org/abs/2509.02808","url_pdf":"https://arxiv.org/pdf/2509.02808v1","authors":"[\"Isaac Ronald Ward\",\"Mark Paral\",\"Kristopher Riordan\",\"Mykel J. Kochenderfer\"]","published":"2025-09-02T20:22:54Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.AI\",\"eess.SY\"]","methods":"[]","has_code":false}