Fetching the paper…
Reading the bibliography…
We present TartanDrive, a large scale dataset for learning dynamics models for off-road driving.
G. E. Hinton, “Training products of experts by minimizing contrastive divergence,” Neural computation , vol. 14, no. 8, pp. 1771–1800, 2002
2002
Earlier work this paper cites.
T. M. Howard and A. Kelly, “Optimal rough terrain trajectory generation for wheeled mobile robots,” The International Journal of Robotics Research , vol. 26, no. 2, pp. 141–166, 2007
2007
Earlier work this paper cites.
M. Müller, “Dynamic time warping,” Information retrieval for music and motion , pp. 69–84, 2007
2007
Earlier work this paper cites.
L. Van der Maaten and G. Hinton, “Visualizing data using t-sne.” Journal of machine learning research , vol. 9, no. 11, 2008
2008
Earlier work this paper cites.
2014
Earlier work this paper cites.
K. Cho, B. Van Merriënboer, C. Gulcehre et al. , “Learning phrase representations using rnn encoder-decoder for statistical machine translation,” Conference on Empirical Methods in Natural Language Processing , 2014
2014
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in European conference on computer vision . Springer, 2016, pp. 630–645
2016
Earlier work this paper cites.
M. Cordts, M. Omran, S. Ramos et al. , “The cityscapes dataset for semantic urban scene understanding,” in Proc. of the IEEE Conference on Computer Vision and Pattern Recognition , 2016
2016
Earlier work this paper cites.
A. Valada, G. L. Oliveira, T. Brox, and W. Burgard, “Deep multispectral semantic scene understanding of forested environments using multimodal fusion,” in International symposium on experimental robotics . Springer, 2016, pp. 465–477
2016
Earlier work this paper cites.
P. W. Battaglia, R. Pascanu, M. Lai et al. , “Interaction networks for learning about objects, relations and physics,” in NIPS , 2016
2016
Earlier work this paper cites.
A. Lerer, S. Gross, and R. Fergus, “Learning physical intuition of block towers by example,” in International conference on machine learning . PMLR, 2016, pp. 430–438
2016
Earlier work this paper cites.
P. Agrawal, A. Nair, P. Abbeel et al. , “Learning to poke by poking: experiential learning of intuitive physics,” in Proceedings of the 30th International Conference on Neural Information Processing Systems , 2016, pp. 5092–5100
2016
Earlier work this paper cites.
C. Finn, I. Goodfellow, and S. Levine, “Unsupervised learning for physical interaction through video prediction,” Advances in neural information processing systems , vol. 29, pp. 64–72, 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
W. Maddern, G. Pascoe, C. Linegar, and P. Newman, “1 Year, 1000km: The Oxford RobotCar Dataset,” The International Journal of Robotics Research , vol. 36, no. 1, pp. 3–15, 2017. [Online]. Available: http://dx.doi.org/10.1177/0278364916679498
2017
Cited alongside, same era.
D. Maturana, P.-W. Chou, M. Uenoyama, and S. Scherer, “Real-time semantic mapping for autonomous off-road navigation,” in Field and Service Robotics . Springer, 2018, pp. 335–350
2018
Cited alongside, same era.
S. Levine, P. Pastor, A. Krizhevsky et al. , “Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection,” The International Journal of Robotics Research , vol. 37, no. 4-5, pp. 421–436, 2018
2018
Cited alongside, same era.
Z. Pezzementi, T. Tabor, P. Hu et al. , “Comparing apples and oranges: Off-road pedestrian detection on the national robotics engineering center agricultural person-detection dataset,” Journal of Field Robotics , vol. 35, no. 4, pp. 545–563, 2018
2018
H.-Y. F. Tung, R. Cheng, and K. Fragkiadaki, “Learning spatial common sense with geometry-aware recurrent networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 2595–2603
2019
Later among the works it cites.
J.-F. Tremblay, T. Manderson, A. Noca et al. , “Multimodal dynamics modeling for off-road autonomous vehicles,” IEEE International Conference on Robotics and Automation , 2020
2020
Later among the works it cites.
J. Geyer, Y. Kassahun, M. Mahmudi et al. , “A2D2: Audi Autonomous Driving Dataset,” 2020. [Online]. Available: https://www.a2d2.audi
2020
Later among the works it cites.
L. Dabbiru, C. Goodin, N. Scherrer, and D. Carruth, “Lidar data segmentation in off-road environment using convolutional neural networks (cnn),” SAE International Journal of Advances and Current Practices in Mobility , vol. 2, no. 2020-01-0696, pp. 3288–3292, 2020
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
J.-R. Chang and Y.-S. Chen, “Pyramid stereo matching network,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 5410–5418
2018
Cited alongside, same era.
M. Wu and N. Goodman, “Multimodal generative models for scalable weakly-supervised learning,” Advances in Neural Information Processing Systems , 2018
2018
Cited alongside, same era.
“Waymo open dataset: An autonomous driving dataset,” 2019
2019
Cited alongside, same era.
M.-F. Chang, J. W. Lambert, P. Sangkloy et al. , “Argoverse: 3d tracking and forecasting with rich maps,” in Conference on Computer Vision and Pattern Recognition , 2019
2019
Cited alongside, same era.
M. Wigness, S. Eum, J. G. Rogers et al. , “A rugd dataset for autonomous navigation and visual perception in unstructured outdoor environments,” in International Conference on Intelligent Robots and Systems , 2019
2019
Cited alongside, same era.
F. Baradel, N. Neverova, J. Mille et al. , “Cophy: Counterfactual learning of physical dynamics,” in International Conference on Learning Representations , 2019
2019
Cited alongside, same era.
J.-F. Tremblay, M. Béland, F. Pomerleau et al. , “Automatic 3d mapping for tree diameter measurements in inventory operations,” Journal of Field Robotics , 2019
2019
Cited alongside, same era.
D. Hafner, T. Lillicrap, J. Ba, and M. Norouzi, “Dream to control: Learning behaviors by latent imagination,” International Conference on Learning Representations , 2019
2019
Cited alongside, same era.
P. Jiang, P. Osteen, M. Wigness, and S. Saripalli, “Rellis-3d dataset: Data, benchmarks and analysis,” 2020
2020
Later among the works it cites.
D. Hafner, T. Lillicrap, M. Norouzi, and J. Ba, “Mastering atari with discrete world models,” International Conference on Learning Representations , 2020
2020
Later among the works it cites.
J. Mai, “System design, modelling, and control for an off-road autonomous ground vehicle,” Master’s thesis, Carnegie Mellon University, Pittsburgh, PA, July 2020
2020
Later among the works it cites.
W. Wang, Y. Hu, and S. Scherer, “Tartanvo: A generalizable learning-based vo,” Conference on Robot Learning , 2020
2020
Later among the works it cites.
R. Tavenard, J. Faouzi, G. Vandewiele et al. , “Tslearn, a machine learning toolkit for time series data,” Journal of Machine Learning Research , vol. 21, no. 118, pp. 1–6, 2020. [Online]. Available: http://jmlr.org/papers/v21/20-091.html
2020
Later among the works it cites.
G. Kahn, P. Abbeel, and S. Levine, “Badgr: An autonomous self-supervised learning-based navigation system,” IEEE Robotics and Automation Letters , vol. 6, no. 2, pp. 1312–1319, 2021
2021
Later among the works it cites.
S. J. Wang, S. Triest, W. Wang et al. , “Rough terrain navigation using divergence constrained model based reinforcement learning,” in Conference on Robot Learning . PMLR, 2021
2021
Later among the works it cites.
M. Sivaprakasam, S. Triest, W. Wang et al. , “Improving off-road planning techniques with learned costs from physical interactions,” in Proceedings - IEEE International Conference on Robotics and Automation , Xi’an, China, May 2021
2021
Later among the works it cites.
A. Shaban, X. Meng, J. Lee et al. , “Semantic terrain classification for off-road autonomous driving,” in Conference on Robot Learning . PMLR, 2022, pp. 619–629
2022
Closest in time.