2019/09/28 by Changan Chen, Chen, Changan, Sha Hu +7 · 9 citations
Computer Science · Engineering · #Anomaly Detection Techniques and Applications #Artificial Intelligence (cs.AI) #Artificial intelligence #Baseline (sea) #Computer science #Computer security #Convolutional neural network #Crowds #ENCODE #Evacuation and Crowd Dynamics #Exploit #FOS: Computer and information sciences #Feature learning #Graph #Human Pose and Action Recognition #Machine Learning (cs.LG) #Machine learning #Reinforcement learning #Representation (politics) #Robotics (cs.RO) #Theoretical computer science #cs.AI #cs.LG #cs.RO
paper · pdf · doi:10.48550/arxiv.1909.13165
published in arXiv (Cornell University) (Cornell University) · Accepted to IROS 2020. Added links to codes and video demo
openalex publication_date 2019/09/28 · arxiv created 2020/08/03 · arxiv updated 2020/08/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We present a relational graph learning approach for robotic crowd navigation using model-based deep reinforcement learning that plans actions by looking into the future. Our approach reasons about the relations between all agents based on their latent features and uses a Graph Convolutional Network to encode higher-order interactions in each agent's state representation, which is subsequently leveraged for state prediction and value estimation. The ability to predict human motion allows us to perform multi-step lookahead planning, taking into account the temporal evolution of human crowds. We evaluate our approach against a state-of-the-art baseline for crowd navigation and ablations of our model to demonstrate that navigation with our approach is more efficient, results in fewer collisions, and avoids failure cases involving oscillatory and freezing behaviors.