{"ID":2832604,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.05808","arxiv_id":"2512.05808","title":"Real-time Remote Tracking and Autonomous Planning for Whale Rendezvous using Robots","abstract":"We introduce a system for real-time sperm whale rendezvous at sea using an autonomous uncrewed aerial vehicle. Our system employs model-based reinforcement learning that combines in situ sensor data with an empirical whale dive model to guide navigation decisions. Key challenges include (i) real-time acoustic tracking in the presence of multiple whales, (ii) distributed communication and decision-making for robot deployments, and (iii) on-board signal processing and long-range detection from fish-trackers. We evaluate our system by conducting rendezvous with sperm whales at sea in Dominica, performing hardware experiments on land, and running simulations using whale trajectories interpolated from marine biologists' surface observations.","short_abstract":"We introduce a system for real-time sperm whale rendezvous at sea using an autonomous uncrewed aerial vehicle. Our system employs model-based reinforcement learning that combines in situ sensor data with an empirical whale dive model to guide navigation decisions. Key challenges include (i) real-time acoustic tracking...","url_abs":"https://arxiv.org/abs/2512.05808","url_pdf":"https://arxiv.org/pdf/2512.05808v1","authors":"[\"Sushmita Bhattacharya\",\"Ninad Jadhav\",\"Hammad Izhar\",\"Karen Li\",\"Kevin George\",\"Robert Wood\",\"Stephanie Gil\"]","published":"2025-12-05T15:27:58Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}