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2019

Relational Graph Learning for Crowd Navigation

Chen, Changan, Hu, Sha, Nikdel, Payam et al.

Understand

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.

Built on

P. Cai, Y. Luo, A. Saxena, D. Hsu, and W. S. Lee, “LeTS-drive: Driving in a crowd by learning from tree search,” in
Original
1905
Earlier work this paper cites.
A. Agarwal, S. Kumar, and K. Sycara, “Learning transferable cooperative behavior in multi-agent teams,” in
Original
1906
Earlier work this paper cites.
J. v. d. Berg, Ming Lin, and D. Manocha, “Reciprocal velocity obstacles for real-time multi-agent navigation,” in
1935
Earlier work this paper cites.
P. Wieber, “Trajectory free linear model predictive control for stable walking in the presence of strong perturbations,” in
2006
Earlier work this paper cites.
P. Trautman and A. Krause, “Unfreezing the robot: Navigation in dense, interacting crowds,” in
2010
Earlier work this paper cites.
P. Trautman, J. Ma, R. M. Murray, and A. Krause, “Robot navigation in dense human crowds: the case for cooperation,” in
2013
Earlier work this paper cites.
Y.-W. Chao, Z. Wang, Y. He, J. Wang, and J. Deng, “HICO: A benchmark for recognizing human-object interactions in images,” in
2015
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in
Original
2015
Earlier work this paper cites.
A. Alahi, K. Goel, V. Ramanathan, A. Robicquet, L. Fei-Fei, and S. Savarese, “Social LSTM: Human trajectory prediction in crowded spaces,” in
2016
Earlier work this paper cites.
Z. Deng, A. Vahdat, H. Hu, and G. Mori, “Structure inference machines: Recurrent neural networks for analyzing relations in group activity recognition,” in
Original
2016
Earlier work this paper cites.
P. W. Battaglia, R. Pascanu, M. Lai, D. Rezende, and K. Kavukcuoglu, “Interaction networks for learning about objects, relations and physics,” in
Original
2016
Earlier work this paper cites.
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, “Mastering the game of go with deep neural networks and tree search,” in
2016
Earlier work this paper cites.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in
Original
2016
Earlier work this paper cites.

Similar

Y. F. Chen, M. Liu, M. Everett, and J. P. How, “Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning,” in
Original
2017
Cited alongside, same era.
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in
Original
2017
Cited alongside, same era.
P. Trautman, “Sparse interacting gaussian processes: Efficiency and optimality theorems of autonomous crowd navigation,” in
2017
Cited alongside, same era.
Y. F. Chen, M. Everett, M. Liu, and J. P. How, “Socially aware motion planning with deep reinforcement learning,” in
Original
2017
Cited alongside, same era.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in

Then

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alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

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D. Silver, H. van Hasselt, M. Hessel, T. Schaul, A. Guez, T. Harley, G. Dulac-Arnold, D. Reichert, N. Rabinowitz, A. Barreto, and T. Degris, “The predictron: End-to-end learning and planning,” in

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V. Zambaldi, D. Raposo, A. Santoro, V. Bapst, Y. Li, I. Babuschkin, K. Tuyls, D. Reichert, T. Lillicrap, E. Lockhart, M. Shanahan, V. Langston, R. Pascanu, M. Botvinick, O. Vinyals, and P. Battaglia, “Relational deep reinforcement learning,” in

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G. Gkioxari, R. Girshick, P. Dollár, and K. He, “Detecting and recognizing human-object interactions,” in

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