Crowd-Robot Interaction: Crowd-Aware Robot Navigation With Attention-Based Deep Reinforcement Learning
By Changan Chen, Yuejiang Liu, Sven Kreiss, Alexandre Alahi. IEEE International Conference on Robotics and Automation, 2018
660 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computer Science Applications, Ocean Engineering
View Paper: https://www.semanticscholar.org/paper/8443865a2264d753ae0bbdc1a3137f8f7c605f08
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In Ocean Engineering
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Citations in the corpus
- Core Challenges of Social Robot Navigation: A Survey (2021, methodology): “For instance, the majority of recent literature [30, 31, 53, 136] relies on ORCA [239] as the main behavioral engine governing simulated humans for both training and testing of learning algorithms.”
- Robot Navigation in Constrained Pedestrian Environments using Reinforcement Learning (2020, methodology): “in autonomous navigation and human-robot interaction to study different aspects of the navigation problem, such as navigation within crowds [7], communicating implicit and explicit navigation information with haptic devices [8], conavigation [9] using anticipatory signals [10], navigation cues in hu”