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Humans excel at efficiently navigating through crowds without collision by focusing on specific visual regions relevant to navigation.
D. A. Pomerleau, “ALVINN: An autonomous land vehicle in a neural network,” in Advances in Neural Information Processing Systems , D. Touretzky, Ed., vol. 1. Morgan-Kaufmann, 1988
1988
Earlier work this paper cites.
J. Bromley, I. Guyon, Y. LeCun, E. Säckinger, and R. Shah, “Signature verification using a ”Siamese” time delay neural network,” in Advances in Neural Information Processing Systems , J. Cowan, G. Tesauro, and J. Alspector, Eds., vol. 6. Morgan-Kaufmann, 1993
1993
Earlier work this paper cites.
S. Quinlan and O. Khatib, “Elastic bands: Connecting path planning and control,” in [1993] Proceedings IEEE International Conference on Robotics and Automation . IEEE, 1993, pp. 802–807
1993
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, 1997
1997
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition , Jun. 2009, pp. 248–255
2009
Earlier work this paper cites.
Y. Bengio, A. Courville, and P. Vincent, “Representation Learning: A Review and New Perspectives,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 35, no. 8, pp. 1798–1828, Aug. 2013, conference Name: IEEE Transactions on Pattern Analysis and Machine Intelligence
2013
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 770–778, 2015
2015
Earlier work this paper cites.
M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, X. Zhang, J. Zhao, and K. Zieba, “End to End Learning for Self-Driving Cars,” Apr. 2016
2016
Earlier work this paper cites.
A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “CARLA: An open urban driving simulator,” in Proceedings of the 1st Annual Conference on Robot Learning , 2017, pp. 1–16
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
F. Codevilla, M. Muller, A. Lopez, V. Koltun, and A. Dosovitskiy, “End-to-End Driving Via Conditional Imitation Learning,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . Brisbane, QLD: IEEE, May 2018, pp. 4693–4700
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
A. R. Zamir, A. Sax, W. Shen, L. J. Guibas, J. Malik, and S. Savarese, “Taskonomy: Disentangling task transfer learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , Jun. 2018
2018
Earlier work this paper cites.
F. Codevilla, E. Santana, A. M. Lopez, and A. Gaidon, “Exploring the Limitations of Behavior Cloning for Autonomous Driving,” in The IEEE International Conference on Computer Vision (ICCV) . IEEE, Oct. 2019
2019
Earlier work this paper cites.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) , J. Burstein, C. Doran, and T. Solorio, Eds. Minneapolis, Minnesota: Association for Computational Linguistics, Jun. 2019, pp. 4171–4186
2019
Earlier work this paper cites.
W. Shen, D. Xu, Y. Zhu, L. Fei-Fei, L. Guibas, and S. Savarese, “Situational Fusion of Visual Representation for Visual Navigation,” in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) . Seoul, Korea (South): IEEE, Oct. 2019, pp. 2881–2890
2019
Earlier work this paper cites.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “PyTorch: An imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems 32 . Curran Associates, Inc., 2019, pp. 8024–8035
2019
Earlier work this paper cites.
I. Loshchilov and F. Hutter, “Decoupled Weight Decay Regularization,” Jan. 2019
2019
Earlier work this paper cites.
E. Ohn-Bar, A. Prakash, A. Behl, K. Chitta, and A. Geiger, “Learning Situational Driving,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, Jun. 2020
2020
Earlier work this paper cites.
D. Chen, B. Zhou, V. Koltun, and P. Krähenbühl, “Learning by cheating,” in Proceedings of the Conference on Robot Learning , ser. Proceedings of Machine Learning Research, L. P. Kaelbling, D. Kragic, and K. Sugiura, Eds., vol. 100. PMLR, 2020-11-30, 2020, pp. 66–75
2020
Earlier work this paper cites.
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in International Conference on Machine Learning . PMLR, 2020, pp. 1597–1607
2020
Earlier work this paper cites.
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, “Momentum contrast for unsupervised visual representation learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 9729–9738
2020
Earlier work this paper cites.
J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. Richemond, E. Buchatskaya, C. Doersch, B. Avila Pires, Z. Guo, M. Gheshlaghi Azar et al. , “Bootstrap your own latent-a new approach to self-supervised learning,” Advances in neural information processing systems , vol. 33, pp. 21 271–21 284, 2020
2020
Earlier work this paper cites.
X. Chen and K. He, “Exploring Simple Siamese Representation Learning,” Nov. 2020
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
M. H. Nazeri and M. Bohlouli, “Exploring Reflective Limitation of Behavior Cloning in Autonomous Vehicles,” in 2021 IEEE International Conference on Data Mining (ICDM) . Auckland, New Zealand: IEEE, Dec. 2021, pp. 1252–1257
2021
Earlier work this paper cites.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,” Jun. 2021
2021
Cited alongside, same era.
J. Zbontar, L. Jing, I. Misra, Y. LeCun, and S. Deny, “Barlow Twins: Self-Supervised Learning via Redundancy Reduction,” Jun. 2021
2021
Cited alongside, same era.
D. Chen, V. Koltun, and P. Krähenbühl, “Learning to drive from a world on rails,” in ICCV , 2021
2021
Cited alongside, same era.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger, and I. Sutskever, “Learning transferable visual models from natural language supervision,” in Proceedings of the 38th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, M. Meila and T. Zhang, Eds., vol. 139. PMLR, Jul. 2021, pp. 8748–8763
2021
A. Bardes, J. Ponce, and Y. LeCun, “MC-JEPA: A Joint-Embedding Predictive Architecture for Self-Supervised Learning of Motion and Content Features,” Jul. 2023
2023
Later among the works it cites.
R. Balestriero, M. Ibrahim, V. Sobal, A. Morcos, S. Shekhar, T. Goldstein, F. Bordes, A. Bardes, G. Mialon, Y. Tian, A. Schwarzschild, A. G. Wilson, J. Geiping, Q. Garrido, P. Fernandez, A. Bar, H. Pirsiavash, Y. LeCun, and M. Goldblum, “A Cookbook of Self-Supervised Learning,” Apr. 2023
2023
Later among the works it cites.
B. Jaeger and A. Geiger, “An Invitation to Deep Reinforcement Learning,” Dec. 2023
2023
Later among the works it cites.
A. Payandeh, K. T. Baghaei, P. Fayyazsanavi, S. B. Ramezani, Z. Chen, and S. Rahimi, “Deep representation learning: Fundamentals, technologies, applications, and open challenges,” IEEE Access , vol. 11, pp. 137 621–137 659, 2023
2023
Later among the works it cites.
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Cited alongside, same era.
X. Xiao, B. Liu, G. Warnell, and P. Stone, “Toward agile maneuvers in highly constrained spaces: Learning from hallucination,” IEEE Robotics and Automation Letters , vol. 6, no. 2, pp. 1503–1510, 2021
2021
Cited alongside, same era.
B. Liu, X. Xiao, and P. Stone, “A lifelong learning approach to mobile robot navigation,” IEEE Robotics and Automation Letters , vol. 6, no. 2, pp. 1090–1096, 2021
2021
Cited alongside, same era.
X. Xiao, J. Biswas, and P. Stone, “Learning inverse kinodynamics for accurate high-speed off-road navigation on unstructured terrain,” IEEE Robotics and Automation Letters , vol. 6, no. 3, pp. 6054–6060, 2021
2021
Cited alongside, same era.
X. Xiao, B. Liu, G. Warnell, and P. Stone, “Motion planning and control for mobile robot navigation using machine learning: a survey,” Autonomous Robots , vol. 46, no. 5, pp. 569–597, 2022
2022
Cited alongside, same era.
K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick, “Masked autoencoders are scalable vision learners,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 16 000–16 009
2022
Cited alongside, same era.
A. Bardes, J. Ponce, and Y. LeCun, “VICReg: Variance-invariance-covariance regularization for self-supervised learning,” in International Conference on Learning Representations , 2022
2022
Cited alongside, same era.
H. Karnan, A. Nair, X. Xiao, G. Warnell, S. Pirk, A. Toshev, J. Hart, J. Biswas, and P. Stone, “Socially compliant navigation dataset (scand): A large-scale dataset of demonstrations for social navigation,” IEEE Robotics and Automation Letters , vol. 7, no. 4, pp. 11 807–11 814, 2022
2022
Cited alongside, same era.
K. S. Sikand, S. Rabiee, A. Uccello, X. Xiao, G. Warnell, and J. Biswas, “Visual representation learning for preference-aware path planning,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 11 303–11 309
2022
Cited alongside, same era.
J. Luo, P. Dong, Y. Zhai, Y. Ma, and S. Levine, “RLIF: Interactive Imitation Learning as Reinforcement Learning,” Nov. 2023
2023
Later among the works it cites.
W. Huang, Y. Zhou, X. He, and C. Lv, “Goal-guided Transformer-enabled Reinforcement Learning for Efficient Autonomous Navigation,” Jan. 2023
2023
Later among the works it cites.
A. Brohan, N. Brown, J. Carbajal, Y. Chebotar, J. Dabis, C. Finn, K. Gopalakrishnan, K. Hausman, A. Herzog, J. Hsu, J. Ibarz, B. Ichter, A. Irpan, T. Jackson, S. Jesmonth, N. J. Joshi, R. Julian, D. Kalashnikov, Y. Kuang, I. Leal, K.-H. Lee, S. Levine, Y. Lu, U. Malla, D. Manjunath, I. Mordatch, O. Nachum, C. Parada, J. Peralta, E. Perez, K. Pertsch, J. Quiambao, K. Rao, M. Ryoo, G. Salazar, P. Sanketi, K. Sayed, J. Singh, S. Sontakke, A. Stone, C. Tan, H. Tran, V. Vanhoucke, S. Vega, Q. Vuong, F. Xia, T. Xiao, P. Xu, S. Xu, T. Yu, and B. Zitkovich, “RT-1: Robotics Transformer for Real-World Control at Scale,” Aug. 2023
2023
Later among the works it cites.
N. Di Palo, A. Byravan, L. Hasenclever, M. Wulfmeier, N. Heess, and M. Riedmiller, “Towards A Unified Agent with Foundation Models,” Jul. 2023
2023
Later among the works it cites.
A. Hiranaka, M. Hwang, S. Lee, C. Wang, L. Fei-Fei, J. Wu, and R. Zhang, “Primitive Skill-based Robot Learning from Human Evaluative Feedback,” Aug. 2023
2023
Later among the works it cites.
S. Nair, A. Rajeswaran, V. Kumar, C. Finn, and A. Gupta, “R3M: A universal visual representation for robot manipulation,” in Conference on Robot Learning . PMLR, 2023, pp. 892–909
2023
Later among the works it cites.
H.-K. Yang, T.-C. Chiang, T.-R. Liu, C.-W. Huang, J.-M. Liu, and C.-Y. Lee, “Virtual Guidance as a Mid-level Representation for Navigation,” Sep. 2023
2023
Later among the works it cites.
H. Karnan, E. Yang, D. Farkash, G. Warnell, J. Biswas, and P. Stone, “STERLING: Self-Supervised Terrain Representation Learning from Unconstrained Robot Experience,” Oct. 2023
2023
Later among the works it cites.
A. Eftekhar, K.-H. Zeng, J. Duan, A. Farhadi, A. Kembhavi, and R. Krishna, “Selective Visual Representations Improve Convergence and Generalization for Embodied AI,” Nov. 2023
2023
Later among the works it cites.
Y. Wang, C.-Y. Ko, and P. Agrawal, “Visual pre-training for navigation: What can we learn from noise?” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2023, pp. 3897–3902
2023
Later among the works it cites.
V. Shah, F. Träuble, A. Malik, H. Larochelle, M. Mozer, S. Arora, Y. Bengio, and A. Goyal, “Unlearning via Sparse Representations,” Nov. 2023
2023
Later among the works it cites.
S. Ravi, G. Wang, S. Satewar, X. Xiao, G. Warnell, J. Biswas, and P. Stone, “Visually adaptive geometric navigation,” in 2023 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) . IEEE, 2023
2023
Later among the works it cites.
T. Darcet, M. Oquab, J. Mairal, and P. Bojanowski, “Vision Transformers Need Registers,” Sep. 2023
2023
Later among the works it cites.
D. M. Nguyen, M. Nazeri, A. Payandeh, A. Datar, and X. Xiao, “Toward human-like social robot navigation: A large-scale, multi-modal, social human navigation dataset,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
K. Vishniakov, Z. Shen, and Z. Liu, “ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet Accuracy,” Jan. 2024
2024
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2024
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2024
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M. Nazeri, J. Wang, A. payandeh, and X. Xiao, “VANP: Self-supervised vision-action pretraining for navigation,” in Bridging the Gap between Cognitive Science and Robot Learning in the Real World: Progresses and New Directions , 2024. [Online]. Available: https://openreview.net/forum?id=MOI3CESxR2
2024
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A. Datar, C. Pan, M. Nazeri, and X. Xiao, “Toward wheeled mobility on vertically challenging terrain: Platforms, datasets, and algorithms,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024
2024
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2024
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R. Mirsky, X. Xiao, J. Hart, and P. Stone, “Conflict avoidance in social navigation—a survey,” ACM Transactions on Human-Robot Interaction , vol. 13, no. 1, pp. 1–36, 2024
2024
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