Fetching the paper…
Reading the bibliography…
Collision avoidance algorithms are essential for safe and efficient robot operation among pedestrians.
V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu, “Asynchronous methods for deep reinforcement learning,” in International Conference on Machine Learning , 2016, pp. 1928–1937
1937
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
D. Fox, W. Burgard, and S. Thrun, “The dynamic window approach to collision avoidance,” IEEE Robotics Automation Magazine , vol. 4, no. 1, pp. 23–33, Mar. 1997
1997
Earlier work this paper cites.
H. Kitano, M. Asada, Y. Kuniyoshi, I. Noda, and E. Osawa, “Robocup: The robot world cup initiative,” in Proceedings of the first international conference on Autonomous agents , 1997, pp. 340–347
1997
Earlier work this paper cites.
R. S. Sutton and A. G. Barto, Introduction to Reinforcement Learning , 1st ed. Cambridge, MA, USA: MIT Press, 1998
1998
Earlier work this paper cites.
J. B. Rawlings, “Tutorial overview of model predictive control,” IEEE control systems magazine , vol. 20, no. 3, pp. 38–52, 2000
2000
Earlier work this paper cites.
P. Trautman and A. Krause, “Unfreezing the robot: Navigation in dense, interacting crowds,” in 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , Oct. 2010, pp. 797–803
2010
Earlier work this paper cites.
J. Snape, J. Van den Berg, S. J. Guy, and D. Manocha, “The hybrid reciprocal velocity obstacle,” IEEE Transactions on Robotics , vol. 27, no. 4, pp. 696–706, Aug. 2011
2011
Earlier work this paper cites.
J. Van den Berg, S. J. Guy, M. Lin, and D. Manocha, “Reciprocal n-body collision avoidance,” in Robotics Research , ser. Springer Tracts in Advanced Robotics. Springer Berlin Heidelberg, 2011, no. 70, pp. 3–19
2011
Earlier work this paper cites.
M. Phillips and M. Likhachev, “SIPP: Safe interval path planning for dynamic environments,” in 2011 IEEE International Conference on Robotics and Automation (ICRA) , May 2011, pp. 5628–5635
2011
Earlier work this paper cites.
M. Kuderer, H. Kretzschmar, C. Sprunk, and W. Burgard, “Feature-based prediction of trajectories for socially compliant navigation,” in Robotics:Science and Systems , 2012
2012
Earlier work this paper cites.
R. Kümmerle, M. Ruhnke, B. Steder, C. Stachniss, and W. Burgard, “A navigation system for robots operating in crowded urban environments,” in 2013 IEEE International Conference on Robotics and Automation . IEEE, 2013, pp. 3225–3232
2013
Earlier work this paper cites.
G. Ferrer, A. Garrell, and A. Sanfeliu, “Social-aware robot navigation in urban environments,” in 2013 European Conference on Mobile Robots (ECMR) , Sep. 2013, pp. 331–336
2013
Earlier work this paper cites.
J. Alonso-Mora, A. Breitenmoser, M. Rufli, P. Beardsley, and R. Siegwart, “Optimal reciprocal collision avoidance for multiple non-holonomic robots,” in Distributed Autonomous Robotic Systems . Springer, 2013, pp. 203–216
2013
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 Proceedings of the 2013 IEEE International Conference on Robotics and Automation (ICRA) , May 2013, pp. 2153–2160
2013
Earlier work this paper cites.
G. S. Aoude, B. D. Luders, J. M. Joseph, N. Roy, and J. P. How, “Probabilistically safe motion planning to avoid dynamic obstacles with uncertain motion patterns,” Autonomous Robots , vol. 35, no. 1, pp. 51–76, May 2013
2013
Earlier work this paper cites.
T. Campbell, M. Liu, B. Kulis, J. P. How, and L. Carin, “Dynamic clustering via asymptotics of the dependent dirichlet process mixture,” in Advances in Neural Information Processing Systems , 2013, pp. 449–457
2013
Cited alongside, same era.
I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in Advances in neural information processing systems , 2014, pp. 3104–3112
2014
Cited alongside, same era.
2014
Cited alongside, same era.
2014
Cited alongside, same era.
L. Tai, G. Paolo, and M. Liu, “Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation,” in Intelligent Robots and Systems (IROS), 2017 IEEE/RSJ International Conference on . IEEE, 2017, pp. 31–36
2017
Later among the works it cites.
Y. Chen, M. Liu, M. Everett, and J. P. How, “Decentralized, non-communicating multiagent collision avoidance with deep reinforcement learning,” in Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA) , Singapore, 2017
2017
Later among the works it cites.
Y. F. Chen, M. Everett, M. Liu, and J. P. How, “Socially aware motion planning with deep reinforcement learning,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , Vancouver, BC, Canada, September 2017
2017
Later among the works it cites.
M. Babaeizadeh, I. Frosio, S. Tyree, J. Clemons, and J. Kautz, “Reinforcement learning thorugh asynchronous advantage actor-critic on a gpu,” in ICLR , 2017
2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis, “Human-level control through deep reinforcement learning,” Nature , vol. 518, no. 7540, pp. 529–533, Feb. 2015
2015
Cited alongside, same era.
B. Kim and J. Pineau, “Socially adaptive path planning in human environments using inverse reinforcement learning,” International Journal of Social Robotics , vol. 8, no. 1, pp. 51–66, Jun. 2015
2015
Cited alongside, same era.
J. Garcıa and F. Fernández, “A comprehensive survey on safe reinforcement learning,” Journal of Machine Learning Research , vol. 16, no. 1, pp. 1437–1480, 2015
2015
Cited alongside, same era.
C. Olah, “Understanding lstm networks,” COURSERA: Neural Networks for Machine Learning, 2015
2015
Cited alongside, same era.
S. Omidshafiei, A. akbar Agha-mohammadi, Y. F. Chen, N. K. Ure, J. How, J. Vian, and R. Surati, “MAR-CPS: Measurable Augmented Reality for Prototyping Cyber-Physical Systems,” in AIAA Infotech@ Aerospace , 2015
2015
Cited alongside, same era.
M. Everett, “Robot designed for socially acceptable navigation,” Master Thesis, MIT, Cambridge, MA, USA, Jun. 2017
2015
Cited alongside, same era.
H. Kretzschmar, M. Spies, C. Sprunk, and W. Burgard, “Socially compliant mobile robot navigation via inverse reinforcement learning,” The International Journal of Robotics Research , Jan. 2016
2016
Cited alongside, same era.
2016
Cited alongside, same era.
Later among the works it cites.
2017
Later among the works it cites.
2017
Later among the works it cites.
P. Long, T. Fanl, X. Liao, W. Liu, H. Zhang, and J. Pan, “Towards optimally decentralized multi-robot collision avoidance via deep reinforcement learning,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2018, pp. 6252–6259
2018
Later among the works it cites.
M. Everett, Y. F. Chen, and J. P. How, “Motion planning among dynamic, decision-making agents with deep reinforcement learning,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , Madrid, Spain, September 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
M. Hessel, J. Modayil, H. Van Hasselt, T. Schaul, G. Ostrovski, W. Dabney, D. Horgan, B. Piot, M. Azar, and D. Silver, “Rainbow: Combining improvements in deep reinforcement learning,” in Thirty-Second AAAI Conference on Artificial Intelligence , 2018
2018
Later among the works it cites.
U. Lau, “rl-collision-avoidance,” https://github.com/Acmece/rl-collision-avoidance , 2019, [Online; accessed 10-Sep-2019]
2019
Closest in time.
Intel, “Intel Drones Light Up the Sky,” https://www.intel.com/content/www/us/en/technology-innovation/aerial-technology-light-show.html , 2019, [Online; accessed 4-Sep-2019]
2019
Closest in time.
Airbus, “Airbus Commercial Aircraft formation flight: 50-year anniversary,” https://www.youtube.com/watch?v=JS6w-DXiZpk , 2019, [Online; accessed 4-Sep-2019]
2019
Closest in time.
Pixar, “Finding Nemo (School of Fish Scene),” https://www.youtube.com/watch?v=Le13by2WM70 , 2003, [Online; accessed 4-Sep-2019]
2019
Closest in time.
B. Baker, I. Kanitscheider, T. Markov, Y. Wu, G. Powell, B. McGrew, and I. Mordatch, “Emergent tool use from multi-agent autocurricula,” in International Conference on Learning Representations , 2020. [Online]. Available: https://openreview.net/forum?id=SkxpxJBKwS
2020
Closest in time.
M. Pfeiffer, U. Schwesinger, H. Sommer, E. Galceran, and R. Siegwart, “Predicting actions to act predictably: Cooperative partial motion planning with maximum entropy models,” in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2016, pp. 2096–2101
2096
Closest in time.