Learning When to Adapt: Reactivity-Conditioned Residual Adaptation Under Interaction Shift

Pedestrians differ in whether they respond to a mobile robot, and these responses can change during navigation. Policies trained under a single response assumption may therefore be unnecessarily conservative or rely on cooperation that does not occur. We propose reactivity-conditioned residual adaptation (RCRA), which uses explicit estimates of pedestrian reactivity to modulate a learned residual around a frozen conservative navigation policy. A Transformer estimates per-pedestrian reactivity from motion history and robot context, while spatial aggregation determines the residual strength without online gradient updates. Across five ORCA-based interaction regimes, the estimator achieves 84.78% scenario-averaged classification accuracy. In heterogeneous and changing local interactions, replacing a Bayesian reactivity estimator with our learned estimator reduces mean collision rate from 6.56% to 4.85% and mean path length by 3.6%, with navigation components otherwise unchanged. Across all five regimes, RCRA reduces mean navigation time by 16.8% and path length by 16.7% relative to the conservative backbone, while mean success rate decreases from 98.35% to 96.05%. On recorded PeRoI trajectories, our estimator improves balanced accuracy over the Bayesian reactivity estimator by 4.2 percentage points. Controlled robot trials with prescribed reactivity labels validate the residual-modulation mechanism on hardware. These results support explicit reactivity estimation as a useful signal for policy modulation under heterogeneous and changing local interactions.