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arXiv · 2510.12662

Maximal Adaptation, Minimal Guidance: Permissive Reactive Robot Task Planning with Humans in the Loop

Oz Gitelson·Satya Prakash Nayak·Ritam Raha·Anne-Kathrin Schmuck
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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.

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Oz Gitelson, Satya Prakash Nayak, Ritam Raha, Anne-Kathrin Schmuck. 2025-10-14. Maximal Adaptation, Minimal Guidance: Permissive Reactive Robot Task Planning with Humans in the Loop. https://arxiv.org/abs/2510.12662

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