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

We present a novel framework for human-robot 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) maximal adaptation enables the robot to adjust its strategy online to exploit human behavior for cooperation whenever possible, and (ii) 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, emergent cooperative behaviors beyond the reach of existing approaches, while maintaining strong formal guarantees.