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Creating accurate spatial representations that take into account uncertainty is critical for autonomous robots to safely navigate in unstructured environments.
R. Sinkhorn and P. Knopp, “Concerning nonnegative matrices and doubly stochastic matrices,” Pacific Journal of Mathematics , vol. 21, no. 2, pp. 343–348, 1967
1967
Earlier work this paper cites.
A. Elfes, “Occupancy grids: a probabilistic framework for robot perception and navigation,” Ph.D. dissertation, Carnegie Mellon University, 1989
1989
Earlier work this paper cites.
P. J. Besl and N. D. McKay, “Method for registration of 3-d shapes,” in Sensor fusion IV: control paradigms and data structures , vol. 1611. International Society for Optics and Photonics, 1992, pp. 586–606
1992
Earlier work this paper cites.
D. Arbuckle, A. Howard, and M. Mataric, “Temporal occupancy grids: a method for classifying the spatio-temporal properties of the environment,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , vol. 1, 2002, pp. 409–414
2002
Earlier work this paper cites.
C. Bishop, Pattern Recognition and Machine Learning . Springer, 2006
2006
Earlier work this paper cites.
C. Villani, Optimal transport: old and new . Springer Science & Business Media, 2008, vol. 338
2008
Earlier work this paper cites.
T. Hofmann, B. Schölkopf, and A. J. Smola, “Kernel methods in machine learning,” The Annals of Statistics , pp. 1171–1220, 2008
2008
Earlier work this paper cites.
S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Transactions on Knowledge and Data Engineering , vol. 22, no. 10, pp. 1345–1359, 2009
2009
Earlier work this paper cites.
M. Deisenroth and C. E. Rasmussen, “Pilco: A model-based and data-efficient approach to policy search,” in International Conference on Machine Learning (ICML) , 2011, pp. 465–472
2011
Earlier work this paper cites.
S. T. O’Callaghan and F. T. Ramos, “Gaussian process occupancy maps,” International Journal of Robotics Research (IJRR) , vol. 31, no. 1, pp. 42–62, 2012
2012
Earlier work this paper cites.
M. Cuturi, “Sinkhorn distances: Lightspeed computation of optimal transport,” in Advances in Neural Information Processing Systems (NIPS) , 2013, pp. 2292–2300
2013
Earlier work this paper cites.
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The kitti dataset,” International Journal of Robotics Research (IJRR) , vol. 32, no. 11, pp. 1231–1237, 2013
2013
Earlier work this paper cites.
F. Meier, P. Hennig, and S. Schaal, “Efficient bayesian local model learning for control,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2014, pp. 2244–2249
2014
Earlier work this paper cites.
A. K. Akametalu, S. Kaynama, J. F. Fisac, M. N. Zeilinger, J. H. Gillula, and C. J. Tomlin, “Reachability-based safe learning with gaussian processes,” in IEEE Conference on Decision and Control (CDC) , 2014, pp. 443–455
2014
Earlier work this paper cites.
H. B. Ammar, E. Eaton, J. M. Luna, and P. Ruvolo, “Autonomous cross-domain knowledge transfer in lifelong policy gradient reinforcement learning,” in International Joint Conference on Artificial Intelligence (IJCAI) , 2015, pp. 3345–3351
2015
Earlier work this paper cites.
J. Solomon, F. De Goes, G. Peyré, M. Cuturi, A. Butscher, A. Nguyen, T. Du, and L. Guibas, “Convolutional wasserstein distances: Efficient optimal transportation on geometric domains,” ACM Transactions on Graphics (TOG) , vol. 34, no. 4, pp. 1–11, 2015
2015
Earlier work this paper cites.
F. Ramos and L. Ott, “Hilbert maps: scalable continuous occupancy mapping with stochastic gradient descent,” in Robotics: Science and Systems (RSS) , 2015
2015
Cited alongside, same era.
M. Wüthrich, C. Garcia Cifuentes, S. Trimpe, F. Meier, J. Bohg, J. Issac, and S. Schaal, “Robust gaussian filtering using a pseudo measurement,” in American Control Conference (ACC) , 2016
2016
Cited alongside, same era.
D. Isele, M. Rostami, and E. Eaton, “Using task features for zero-shot knowledge transfer in lifelong learning.” in International Joint Conference on Artificial Intelligence (IJCAI) , 2016, pp. 1620–1626
2016
Cited alongside, same era.
J. Wang and B. Englot, “Fast, accurate gaussian process occupancy maps via test-data octrees and nested bayesian fusion,” in IEEE International Conference on Robotics and Automation (ICRA) , 2016, pp. 1003–1010
2016
Cited alongside, same era.
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” International Conference on Computer Vision (ICCV) , 2017
2017
Later among the works it cites.
T. Kim, M. Cha, H. Kim, J. K. Lee, and J. Kim, “Learning to discover cross-domain relations with generative adversarial networks,” in International Conference on Machine Learning (ICML) , 2017, pp. 1857–1865
2017
Later among the works it cites.
M.-Y. Liu, T. Breuel, and J. Kautz, “Unsupervised image-to-image translation networks,” in Advances in Neural Information Processing Systems (NIPS) , 2017, pp. 700–708
2017
Later among the works it cites.
N. Courty, R. Flamary, D. Tuia, and A. Rakotomamonjy, “Optimal transport for domain adaptation,” IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) , vol. 39, no. 9, pp. 1853–1865, 2017
2017
Later among the works it cites.
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R. Senanayake, L. Ott, S. O’Callaghan, and F. T. Ramos, “Spatio-temporal hilbert maps for continuous occupancy representation in dynamic environments,” in Advances in Neural Information Processing Systems (NIPS) , 2016, pp. 3925–3933
2016
Cited alongside, same era.
2016
Cited alongside, same era.
M. Ghifary, W. B. Kleijn, M. Zhang, D. Balduzzi, and W. Li, “Deep reconstruction-classification networks for unsupervised domain adaptation,” in European Conference on Computer Vision (ECCV) . Springer, 2016, pp. 597–613
2016
Cited alongside, same era.
M. Perrot, N. Courty, R. Flamary, and A. Habrard, “Mapping estimation for discrete optimal transport,” in Advances in Neural Information Processing Systems (NIPS) , 2016, pp. 4197–4205
2016
Cited alongside, same era.
C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in International Conference on Machine Learning (ICML) , 2017, pp. 1126–1135
2017
Cited alongside, same era.
J. Campbell and H. B. Amor, “Bayesian interaction primitives: A slam approach to human-robot interaction,” in Conference on Robot Learning (CoRL) , 2017, pp. 379–387
2017
Cited alongside, same era.
B. Burchfiel and G. Konidaris, “Bayesian eigenobjects: A unified framework for 3d robot perception.” in Robotics: Science and Systems (RSS) , 2017
2017
Cited alongside, same era.
P. A. Lasota, T. Fong, J. A. Shah et al. , “A survey of methods for safe human-robot interaction,” Foundations and Trends in Robotics , vol. 5, no. 4, pp. 261–349, 2017
2017
Cited alongside, same era.
2017
Later among the works it cites.
2017
Later among the works it cites.
——, “Autonomous cross-domain knowledge transfer in lifelong policy gradient reinforcement learning,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) , 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
M. Wulfmeier, A. Bewley, and I. Posner, “Incremental adversarial domain adaptation for continually changing environments,” in IEEE International Conference on Robotics and Automation (ICRA) , 2018, pp. 1–9
2018
Later among the works it cites.
V. V. Unhelkar and J. A. Shah, “Learning models of sequential decision-making without complete state specification using bayesian nonparametric inference and active querying,” Massachusetts Institute of Technology , 2018
2018
Later among the works it cites.
R. Senanayake, A. Tompkins, and F. Ramos, “Automorphing kernels for nonstationarity in mapping unstructured environments,” in Conference on Robot Learning (CoRL) , 2018, pp. 443–455
2018
Later among the works it cites.
G. Vallicrosa and P. Ridao, “H-slam: Rao-blackwellized particle filter slam using hilbert maps,” Sensors , vol. 18, no. 5, 2018
2018
Later among the works it cites.
R. Senanayake and F. Ramos, “Building continuous occupancy maps with moving robots,” in AAAI Conference on Artificial Intelligence (AAAI) , 2018
2018
Later among the works it cites.
K. Bousmalis, A. Irpan, P. Wohlhart, Y. Bai, M. Kelcey, M. Kalakrishnan, L. Downs, J. Ibarz, P. Pastor, K. Konolige et al. , “Using simulation and domain adaptation to improve efficiency of deep robotic grasping,” in IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2018, pp. 4243–4250
2018
Later among the works it cites.
A. Genevay, G. Peyré, and M. Cuturi, “Learning generative models with sinkhorn divergences,” 1608-1617 , 2018
2018
Later among the works it cites.