Navigating the Human Maze: Real-Time Robot Pathfinding with Generative Imitation Learning
This addresses navigation challenges for robots in dynamic, crowded settings, representing an incremental improvement over existing methods.
The paper tackles robot navigation in crowded environments by integrating goal-conditioned generative models with Sampling-based Model Predictive Control, resulting in real-time navigation with reduced collision rates and path lengths.
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.