TY - GEN
T1 - Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph
AU - Liu, Shuijing
AU - Chang, Peixin
AU - Huang, Zhe
AU - Chakraborty, Neeloy
AU - Hong, Kaiwen
AU - Liang, Weihang
AU - McPherson, D. Livingston
AU - Geng, Junyi
AU - Driggs-Campbell, Katherine
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - We study the problem of safe and intention-aware robot navigation in dense and interactive crowds. Most previous reinforcement learning (RL) based methods fail to consider different types of interactions among all agents or ignore the intentions of people, which results in performance degradation. In this paper, we propose a novel recurrent graph neural network with attention mechanisms to capture heterogeneous interactions among agents through space and time. To encourage longsighted robot behaviors, we infer the intentions of dynamic agents by predicting their future trajectories for several timesteps. The predictions are incorporated into a model-free RL framework to prevent the robot from intruding into the intended paths of other agents. We demonstrate that our method enables the robot to achieve good navigation performance and non-invasiveness in challenging crowd navigation scenarios. We successfully transfer the policy learned in simulation to a real-world TurtleBot 2i. Our code and videos are available at https://sites.google.com/view/intention-aware-crowdnav/home.
AB - We study the problem of safe and intention-aware robot navigation in dense and interactive crowds. Most previous reinforcement learning (RL) based methods fail to consider different types of interactions among all agents or ignore the intentions of people, which results in performance degradation. In this paper, we propose a novel recurrent graph neural network with attention mechanisms to capture heterogeneous interactions among agents through space and time. To encourage longsighted robot behaviors, we infer the intentions of dynamic agents by predicting their future trajectories for several timesteps. The predictions are incorporated into a model-free RL framework to prevent the robot from intruding into the intended paths of other agents. We demonstrate that our method enables the robot to achieve good navigation performance and non-invasiveness in challenging crowd navigation scenarios. We successfully transfer the policy learned in simulation to a real-world TurtleBot 2i. Our code and videos are available at https://sites.google.com/view/intention-aware-crowdnav/home.
UR - http://www.scopus.com/inward/record.url?scp=85166560201&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85166560201&partnerID=8YFLogxK
U2 - 10.1109/ICRA48891.2023.10160660
DO - 10.1109/ICRA48891.2023.10160660
M3 - Conference contribution
AN - SCOPUS:85166560201
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 12015
EP - 12021
BT - Proceedings - ICRA 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Robotics and Automation, ICRA 2023
Y2 - 29 May 2023 through 2 June 2023
ER -