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We propose a Gaussian variational inference framework for the motion planning problem.
F. Broussolle, “State estimation in power systems: Detecting bad data through the sparse inverse matrix method,” IEEE Transactions on Power Apparatus and Systems , no. 3, pp. 678–682, 1978
1978
Earlier work this paper cites.
L. E. Kavraki, P. Svestka, J.-C. Latombe, and M. H. Overmars, “Probabilistic roadmaps for path planning in high-dimensional configuration spaces,” IEEE transactions on Robotics and Automation , vol. 12, no. 4, pp. 566–580, 1996
1996
Earlier work this paper cites.
S. Wright, J. Nocedal et al. , “Numerical optimization,” Springer Science , vol. 35, no. 67-68, p. 7, 1999
1999
Earlier work this paper cites.
I. Arasaratnam, S. Haykin, and R. J. Elliott, “Discrete-time nonlinear filtering algorithms using gauss–hermite quadrature,” Proceedings of the IEEE , vol. 95, no. 5, pp. 953–977, 2007
2007
Earlier work this paper cites.
B. D. Ziebart, A. L. Maas, J. A. Bagnell, A. K. Dey et al. , “Maximum entropy inverse reinforcement learning.” in Aaai , vol. 8. Chicago, IL, USA, 2008, pp. 1433–1438
2008
Earlier work this paper cites.
2008
Earlier work this paper cites.
M. Opper and C. Archambeau, “The variational gaussian approximation revisited,” Neural computation , vol. 21, no. 3, pp. 786–792, 2009
2009
Earlier work this paper cites.
N. Ratliff, M. Zucker, J. A. Bagnell, and S. Srinivasa, “Chomp: Gradient optimization techniques for efficient motion planning,” in 2009 IEEE International Conference on Robotics and Automation . IEEE, 2009, pp. 489–494
2009
Earlier work this paper cites.
R. Tedrake, I. R. Manchester, M. Tobenkin, and J. W. Roberts, “Lqr-trees: Feedback motion planning via sums-of-squares verification,” The International Journal of Robotics Research , vol. 29, no. 8, pp. 1038–1052, 2010
2010
Earlier work this paper cites.
T. Hazan and A. Shashua, “Norm-product belief propagation: Primal-dual message-passing for approximate inference,” IEEE Transactions on Information Theory , vol. 56, no. 12, pp. 6294–6316, 2010
2010
Earlier work this paper cites.
M. Kalakrishnan, S. Chitta, E. Theodorou, P. Pastor, and S. Schaal, “Stomp: Stochastic trajectory optimization for motion planning,” in 2011 IEEE international conference on robotics and automation . IEEE, 2011, pp. 4569–4574
2011
Earlier work this paper cites.
J.-C. Latombe, Robot motion planning . Springer Science & Business Media, 2012, vol. 124
2012
Cited alongside, same era.
J. Van Den Berg, S. Patil, and R. Alterovitz, “Motion planning under uncertainty using iterative local optimization in belief space,” The International Journal of Robotics Research , vol. 31, no. 11, pp. 1263–1278, 2012
2012
Cited alongside, same era.
L. P. Kaelbling and T. Lozano-Pérez, “Integrated task and motion planning in belief space,” The International Journal of Robotics Research , vol. 32, no. 9-10, pp. 1194–1227, 2013
2013
Cited alongside, same era.
J. Schulman, J. Ho, A. X. Lee, I. Awwal, H. Bradlow, and P. Abbeel, “Finding locally optimal, collision-free trajectories with sequential convex optimization.” in Robotics: science and systems , vol. 9, no. 1. Citeseer, 2013, pp. 1–10
2013
Cited alongside, same era.
A. Majumdar and R. Tedrake, “Funnel libraries for real-time robust feedback motion planning,” The International Journal of Robotics Research , vol. 36, no. 8, pp. 947–982, 2017
2017
Later among the works it cites.
D. M. Blei, A. Kucukelbir, and J. D. McAuliffe, “Variational inference: A review for statisticians,” Journal of the American statistical Association , vol. 112, no. 518, pp. 859–877, 2017
2017
Later among the works it cites.
M. Mukadam, J. Dong, X. Yan, F. Dellaert, and B. Boots, “Continuous-time gaussian process motion planning via probabilistic inference,” The International Journal of Robotics Research , vol. 37, no. 11, pp. 1319–1340, 2018
2018
Later among the works it cites.
J. R. Magnus and H. Neudecker, Matrix differential calculus with applications in statistics and econometrics . John Wiley & Sons, 2019
2019
Later among the works it cites.
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T. D. Barfoot, C. H. Tong, and S. Särkkä, “Batch continuous-time trajectory estimation as exactly sparse gaussian process regression.” in Robotics: Science and Systems , vol. 10. Citeseer, 2014, pp. 1–10
2014
Cited alongside, same era.
J. Schulman, Y. Duan, J. Ho, A. Lee, I. Awwal, H. Bradlow, J. Pan, S. Patil, K. Goldberg, and P. Abbeel, “Motion planning with sequential convex optimization and convex collision checking,” The International Journal of Robotics Research , vol. 33, no. 9, pp. 1251–1270, 2014
2014
Cited alongside, same era.
Y. Tassa, N. Mansard, and E. Todorov, “Control-limited differential dynamic programming,” in 2014 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2014, pp. 1168–1175
2014
Cited alongside, same era.
M. Mukadam, X. Yan, and B. Boots, “Gaussian process motion planning,” in 2016 IEEE international conference on robotics and automation (ICRA) . IEEE, 2016, pp. 9–15
2016
Cited alongside, same era.
Y. Chen, T. T. Georgiou, and M. Pavon, “Optimal transport over a linear dynamical system,” IEEE Transactions on Automatic Control , vol. 62, no. 5, pp. 2137–2152, 2016
2016
Cited alongside, same era.
——, “On the relation between optimal transport and schrödinger bridges: A stochastic control viewpoint,” Journal of Optimization Theory and Applications , vol. 169, no. 2, pp. 671–691, 2016
2016
Cited alongside, same era.
S. M. LaValle et al. , “Rapidly-exploring random trees: A new tool for path planning.”
Cited in the paper.
T. Osa, “Multimodal trajectory optimization for motion planning,” The International Journal of Robotics Research , vol. 39, no. 8, pp. 983–1001, 2020
2020
Later among the works it cites.
T. D. Barfoot, J. R. Forbes, and D. J. Yoon, “Exactly sparse gaussian variational inference with application to derivative-free batch nonlinear state estimation,” The International Journal of Robotics Research , vol. 39, no. 13, pp. 1473–1502, 2020
2020
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
T. Osa, “Motion planning by learning the solution manifold in trajectory optimization,” The International Journal of Robotics Research , vol. 41, no. 3, pp. 281–311, 2022
2022
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