  This paper addresses navigation in crowded environments by integrating
goal-conditioned generative models with Sampling-based Model Predictive Control
(SMPC). We introduce goal-conditioned autoregressive models to generate crowd
behaviors, capturing intricate interactions among individuals. The model
processes potential robot trajectory samples and predicts the reactions of
surrounding individuals, enabling proactive robotic navigation in complex
scenarios. Extensive experiments show that this algorithm enables real-time
navigation, significantly reducing collision rates and path lengths, and
outperforming selected baseline methods. The practical effectiveness of this
algorithm is validated on an actual robotic platform, demonstrating its
capability in dynamic settings.
