{"ID":2855835,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.12662","arxiv_id":"2510.12662","title":"Maximal Adaptation, Minimal Guidance: Permissive Reactive Robot Task Planning with Humans in the Loop","abstract":"We present a novel framework for human-robot \\emph{logical} interaction that enables robots to reliably satisfy (infinite horizon) temporal logic tasks while effectively collaborating with humans who pursue independent and unknown tasks. The framework combines two key capabilities: (i) \\emph{maximal adaptation} enables the robot to adjust its strategy \\emph{online} to exploit human behavior for cooperation whenever possible, and (ii) \\emph{minimal tunable feedback} enables the robot to request cooperation by the human online only when necessary to guarantee progress. This balance minimizes human-robot interference, preserves human autonomy, and ensures persistent robot task satisfaction even under conflicting human goals. We validate the approach in a real-world block-manipulation task with a Franka Emika Panda robotic arm and in the Overcooked-AI benchmark, demonstrating that our method produces rich, \\emph{emergent} cooperative behaviors beyond the reach of existing approaches, while maintaining strong formal guarantees.","short_abstract":"We present a novel framework for human-robot \\emph{logical} interaction that enables robots to reliably satisfy (infinite horizon) temporal logic tasks while effectively collaborating with humans who pursue independent and unknown tasks. The framework combines two key capabilities: (i) \\emph{maximal adaptation} enables...","url_abs":"https://arxiv.org/abs/2510.12662","url_pdf":"https://arxiv.org/pdf/2510.12662v1","authors":"[\"Oz Gitelson\",\"Satya Prakash Nayak\",\"Ritam Raha\",\"Anne-Kathrin Schmuck\"]","published":"2025-10-14T15:58:42Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}