Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning

Abstract

Mobility in an effective and socially-compliant manner is an essential yet challenging task for robots operating in crowded spaces. Recent works have shown the power of deep reinforcement learning techniques to learn socially cooperative policies. However, their cooperation ability deteriorates as the crowd grows since they typically relax the problem as a one-way Human-Robot interaction problem. In this work, we want to go beyond first-order Human-Robot interaction and more explicitly model Crowd-Robot Interaction (CRI). We propose to (i) rethink pairwise interactions with a self-attention mechanism, and (ii) jointly model Human-Robot as well as Human-Human interactions in the deep reinforcement learning framework. Our model captures the Human-Human interactions occurring in dense crowds that indirectly affects the robot's anticipation capability. Our proposed attentive pooling mechanism learns the collective importance of neighboring humans with respect to their future states. Various experiments demonstrate that our model can anticipate human dynamics and navigate in crowds with time efficiency, outperforming state-of-the-art methods.

Accepted at ICRA2019

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Cited by in corpus (6)

• Conditional Generative Neural System for Probabilistic Trajectory Prediction
• Predicting Responses to a Robot's Future Motion using Generative Recurrent Neural Networks
• Robot Navigation in Crowds by Graph Convolutional Networks with Attention Learned from Human Gaze
• NaviGAN: A Generative Approach for Socially Compliant Navigation
• The Pitfall of More Powerful Autoencoders in Lidar-Based Navigation
• Learning Resilient Behaviors for Navigation Under Uncertainty