Relational Graph Learning for Crowd Navigation

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.

Authors

• Changan Chen
• Sha Hu 0003
• Payam Nikdel
• Greg Mori
• Manolis Savva

Keywords

• Navigation
• Computational modeling
• Reinforcement learning
• Predictive models
• Planning
• Trajectory
• Intelligent robots
• Relation Graph
• Crowd Navigation
• Collision
• Estimated Values
• Multi-agent
• State Value
• Relational Approach
• State Representation
• Deep Reinforcement Learning
• State Prediction
• Graph Convolutional Network
• Human Motion
• Robot Navigation
• Value Function
• Direct Action
• Multilayer Perceptron
• Extra Time
• Translational Motion
• Pairwise Interactions
• Model Predictive Control
• Graph Neural Networks
• Monte Carlo Tree Search
• Challenging Scenarios
• Robot State
• Model-based Reinforcement Learning
• Human-robot Interaction
• Imitation Learning
• Average Return
• Human Trajectory
• Motion Prediction