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We consider the problems of exploration and point-goal navigation in previously unseen environments, where the spatial complexity of indoor scenes and partial observability constitute these tasks challenging.
H. S. Seung, M. Opper, and H. Sompolinsky, “Query by committee,” in Proceedings of the fifth annual workshop on Computational learning theory , 1992, pp. 287–294
1992
Earlier work this paper cites.
B. Yamauchi, “A frontier-based approach for autonomous exploration,” in Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA’97.’Towards New Computational Principles for Robotics and Automation’ . IEEE, 1997, pp. 146–151
1997
Earlier work this paper cites.
S. M. LaValle et al. , “Rapidly-exploring random trees: A new tool for path planning,” 1998
1998
Earlier work this paper cites.
H. J. S. Feder, J. J. Leonard, and C. M. Smith, “Adaptive mobile robot navigation and mapping,” The International Journal of Robotics Research , vol. 18, no. 7, pp. 650–668, 1999
1999
Earlier work this paper cites.
P. Auer, N. Cesa-Bianchi, and P. Fischer, “Finite-time analysis of the multiarmed bandit problem,” Machine learning , vol. 47, no. 2, pp. 235–256, 2002
2002
Earlier work this paper cites.
C. Stachniss, G. Grisetti, and W. Burgard, “Information gain-based exploration using rao-blackwellized particle filters.” in Robotics: Science and systems , vol. 2, 2005, pp. 65–72
2005
Earlier work this paper cites.
N. A. Melchior and R. Simmons, “Particle rrt for path planning with uncertainty,” in Proceedings 2007 IEEE International Conference on Robotics and Automation . IEEE, 2007, pp. 1617–1624
2007
Earlier work this paper cites.
T. Kollar and N. Roy, “Trajectory optimization using reinforcement learning for map exploration,” The International Journal of Robotics Research , vol. 27, no. 2, pp. 175–196, 2008
2008
Earlier work this paper cites.
J.-L. Blanco, J.-A. Fernandez-Madrigal, and J. González, “A novel measure of uncertainty for mobile robot slam with rao—blackwellized particle filters,” The International Journal of Robotics Research , vol. 27, no. 1, pp. 73–89, 2008
2008
Earlier work this paper cites.
K. Ok, S. Ansari, B. Gallagher, W. Sica, F. Dellaert, and M. Stilman, “Path planning with uncertainty: Voronoi uncertainty fields,” in 2013 IEEE International Conference on Robotics and Automation . IEEE, 2013, pp. 4596–4601
2013
Earlier work this paper cites.
L. Carlone, J. Du, M. K. Ng, B. Bona, and M. Indri, “Active slam and exploration with particle filters using kullback-leibler divergence,” Journal of Intelligent & Robotic Systems , vol. 75, no. 2, pp. 291–311, 2014
2014
Earlier work this paper cites.
J. Fuentes-Pacheco, J. Ruiz-Ascencio, and J. M. Rendón-Mancha, “Visual simultaneous localization and mapping: a survey,” Artificial intelligence review , vol. 43, no. 1, pp. 55–81, 2015
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention . Springer, 2015, pp. 234–241
2015
Earlier work this paper cites.
C. Cadena, L. Carlone, H. Carrillo, Y. Latif, D. Scaramuzza, J. Neira, I. Reid, and J. J. Leonard, “Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age,” IEEE Transactions on robotics , vol. 32, no. 6, pp. 1309–1332, 2016
2016
Earlier work this paper cites.
Y. Gal, “Uncertainty in deep learning,” Ph.D. dissertation, University of Cambridge, 2016
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Earlier work this paper cites.
Y. Zhu, R. Mottaghi, E. Kolve, J. J. Lim, A. Gupta, L. Fei-Fei, and A. Farhadi, “Target-driven visual navigation in indoor scenes using deep reinforcement learning,” in 2017 IEEE international conference on robotics and automation (ICRA) . IEEE, 2017, pp. 3357–3364
2017
Earlier work this paper cites.
S. Gupta, J. Davidson, S. Levine, R. Sukthankar, and J. Malik, “Cognitive mapping and planning for visual navigation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 2616–2625
2017
Earlier work this paper cites.
A. Chang, A. Dai, T. Funkhouser, M. Halber, M. Niessner, M. Savva, S. Song, A. Zeng, and Y. Zhang, “Matterport3d: Learning from rgb-d data in indoor environments,” 2017 International Conference on 3D Vision (3DV). IEEE , 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
A. Kendall and Y. Gal, “What uncertainties do we need in bayesian deep learning for computer vision?” in Advances in neural information processing systems , 2017, pp. 5574–5584
2017
Cited alongside, same era.
K. Fang, A. Toshev, L. Fei-Fei, and S. Savarese, “Scene memory transformer for embodied agents in long-horizon tasks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 538–547
2019
Later among the works it cites.
K. Katyal, K. Popek, C. Paxton, P. Burlina, and G. D. Hager, “Uncertainty-aware occupancy map prediction using generative networks for robot navigation,” in 2019 International Conference on Robotics and Automation (ICRA) , 2019, pp. 5453–5459
2019
Later among the works it cites.
2020
Later among the works it cites.
S. K. Ramakrishnan, Z. Al-Halah, and K. Grauman, “Occupancy anticipation for efficient exploration and navigation,” European Conference on Computer Vision , pp. 400–418, 2020
2020
Later among the works it cites.
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M. G. Azar, I. Osband, and R. Munos, “Minimax regret bounds for reinforcement learning,” in International Conference on Machine Learning . PMLR, 2017, pp. 263–272
2017
Cited alongside, same era.
2017
Cited alongside, same era.
B. Lakshminarayanan, A. Pritzel, and C. Blundell, “Simple and scalable predictive uncertainty estimation using deep ensembles,” Advances in Neural Information Processing Systems 30 , 2017
2017
Cited alongside, same era.
Y. Gal, R. Islam, and Z. Ghahramani, “Deep bayesian active learning with image data,” in International Conference on Machine Learning . PMLR, 2017, pp. 1183–1192
2017
Cited alongside, same era.
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” 2017
2017
Cited alongside, same era.
2018
Cited alongside, same era.
F. Xia, A. R. Zamir, Z. He, A. Sax, J. Malik, and S. Savarese, “Gibson env: Real-world perception for embodied agents,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 9068–9079
2018
Cited alongside, same era.
M. Savva, A. Kadian, O. Maksymets, Y. Zhao, E. Wijmans, B. Jain, J. Straub, J. Liu, V. Koltun, J. Malik, et al. , “Habitat: A platform for embodied ai research,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 9339–9347
2019
Cited alongside, same era.
D. S. Chaplot, D. Gandhi, S. Gupta, A. Gupta, and R. Salakhutdinov, “Learning to explore using active neural slam,” International Conference on Learning Representations , 2020
2020
Later among the works it cites.
D. S. Chaplot, D. Gandhi, A. Gupta, and R. Salakhutdinov, “Object goal navigation using goal-oriented semantic exploration,” Advances in Neural Information Processing Systems 33 , 2020
2020
Later among the works it cites.
D. S. Chaplot, R. Salakhutdinov, A. Gupta, and S. Gupta, “Neural topological slam for visual navigation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 12 875–12 884
2020
Later among the works it cites.
M. Narasimhan, E. Wijmans, X. Chen, T. Darrell, D. Batra, D. Parikh, and A. Singh, “Seeing the un-scene: Learning amodal semantic maps for room navigation,” European Conference on Computer Vision. Springer, Cham , 2020
2020
Later among the works it cites.
Y. Katsumata, A. Taniguchi, L. El Hafi, Y. Hagiwara, and T. Taniguchi, “Spcomapgan: Spatial concept formation-based semantic mapping with generative adversarial networks,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 7927–7934
2020
Later among the works it cites.
E. Beeching, J. Dibangoye, O. Simonin, and C. Wolf, “Learning to plan with uncertain topological maps,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part III 16 . Springer, 2020, pp. 473–490
2020
Later among the works it cites.
2021
Later among the works it cites.
P. Karkus, S. Cai, and D. Hsu, “Differentiable slam-net: Learning particle slam for visual navigation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 2815–2825
2021
Later among the works it cites.
2021
Later among the works it cites.
Y. Liang, B. Chen, and S. Song, “SSCNav: Confidence-aware semantic scene completion for visual semantic navigation,” International Conference on Robotics and Automation (ICRA) , 2021
2021
Later among the works it cites.
D. D. Fan, K. Otsu, Y. Kubo, A. Dixit, J. Burdick, and A.-A. Agha-Mohammadi, “Step: Stochastic traversability evaluation and planning for risk-aware off-road navigation,” in Robotics: Science and Systems . RSS Foundation, 2021, pp. 1–21
2021
Later among the works it cites.
È. Pairet, J. D. Hernández, M. Carreras, Y. Petillot, and M. Lahijanian, “Online mapping and motion planning under uncertainty for safe navigation in unknown environments,” IEEE Transactions on Automation Science and Engineering , 2021
2021
Later among the works it cites.
H. Carrillo, I. Reid, and J. A. Castellanos, “On the comparison of uncertainty criteria for active slam,” in 2012 IEEE International Conference on Robotics and Automation . IEEE, 2012, pp. 2080–2087
2087
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