Learning When to Adapt: Reactivity-Conditioned Residual Adaptation under Human-Robot Distribution Shift

Pedestrians differ in whether they respond to a robot, yet many RL policies assume all are non-reactive to prioritize safety. Policies trained without conditioning on local pedestrians' reactivity may therefore be unnecessarily conservative or rely on cooperation that does not occur. We propose RCRA, an online adaptation framework that modulates a learned residual around a frozen conservative navigation policy according to estimated pedestrian reactivity. A Transformer-based estimator infers latent pedestrian reactivity from motion histories and robot context, providing a more accurate signal for navigation adaptation and improving scenario-averaged simulation accuracy by 12.62 percentage points over the Bayesian baseline, which assumes known pedestrian goals. These estimates are spatially aggregated into an adaptation scale that gates LoRA modules, enabling cooperative behavior near reactive pedestrians and conservative behavior near non-reactive pedestrians. Among baselines achieving SR above 90% in every scenario, RCRA reduces scenario-averaged path length by 16.7% relative to the conservative backbone and by 7.3% relative to randomized-training baselines. It improves mean SR by 14.88 percentage points over the reactive-trained policy.