Fetching the paper…
Reading the bibliography…
High-speed autonomous driving in off-road environments has immense potential for various applications, but it also presents challenges due to the complexity of vehicle-terrain interactions.
R. Egele, R. Maulik, K. Raghavan, B. Lusch, I. Guyon, and P. Balaprakash, “AutoDEUQ: Automated deep ensemble with uncertainty quantification,” in Proc. International Conference on Pattern Recognition (ICPR) , 2022, pp. 1908–1914
1914
Earlier work this paper cites.
W. Khalil and E. Dombre, Modeling, identification and control of robots . CRC Press, 2002
2002
Earlier work this paper cites.
M. Tarokh and G. J. McDermott, “Kinematics modeling and analyses of articulated rovers,” IEEE Transactions on Robotics , vol. 21, no. 4, pp. 539–553, 2005
2005
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. Deisenroth and C. E. Rasmussen, “PILCO: A model-based and data-efficient approach to policy search,” in Proc. International Conference on Machine Learning (ICML) , 2011, pp. 465–472
2011
Earlier work this paper cites.
P. Krüsi, P. Furgale, M. Bosse, and R. Siegwart, “Driving on point clouds: Motion planning, trajectory optimization, and terrain assessment in generic nonplanar environments,” Journal of Field Robotics , vol. 34, no. 5, pp. 940–984, 2017
2017
Earlier work this paper cites.
D. Maturana, P.-W. Chou, M. Uenoyama, and S. Scherer, “Real-time semantic mapping for autonomous off-road navigation,” in Proc. International Conference on Field and Service Robotics (FSR) , 2017, pp. 335–350
2017
Earlier work this paper cites.
K. M. Lynch and F. C. Park, Modern robotics . Cambridge University Press, 2017
2017
Earlier work this paper cites.
A. Nagabandi, G. Yang, T. Asmar, R. Pandya, G. Kahn, S. Levine, and R. S. Fearing, “Learning image-conditioned dynamics models for control of underactuated legged millirobots,” in Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2018, pp. 4606–4613
2018
Earlier work this paper cites.
R. O. Chavez-Garcia, J. Guzzi, L. M. Gambardella, and A. Giusti, “Learning ground traversability from simulations,” IEEE Robotics and Automation letters , vol. 3, no. 3, pp. 1695–1702, 2018
2018
Earlier work this paper cites.
P. Fankhauser, M. Bloesch, and M. Hutter, “Probabilistic terrain mapping for mobile robots with uncertain localization,” IEEE Robotics and Automation Letters , vol. 3, no. 4, pp. 3019–3026, 2018
2018
Cited alongside, same era.
G. Williams, P. Drews, B. Goldfain, J. M. Rehg, and E. A. Theodorou, “Information-theoretic model predictive control: Theory and applications to autonomous driving,” IEEE Transactions on Robotics , vol. 34, no. 6, pp. 1603–1622, 2018
2018
Cited alongside, same era.
K. Chua, R. Calandra, R. McAllister, and S. Levine, “Deep reinforcement learning in a handful of trials using probabilistic dynamics models,” Advances in Neural Information Processing Systems , pp. 4754–4765, 2018
2018
Cited alongside, same era.
A. Nagabandi, G. Kahn, R. S. Fearing, and S. Levine, “Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning,” in Proc. IEEE International Conference on Robotics and Automation (ICRA) , 2018, pp. 7559–7566
2018
J.-F. Tremblay, T. Manderson, A. Noca, G. Dudek, and D. Meger, “Multimodal dynamics modeling for off-road autonomous vehicles,” in Proc. IEEE International Conference on Robotics and Automation (ICRA) , 2021, pp. 1796–1802
2021
Later among the works it cites.
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
Later among the works it cites.
J. Betz, H. Zheng, A. Liniger, U. Rosolia, P. Karle, M. Behl, V. Krovi, and R. Mangharam, “Autonomous vehicles on the edge: A survey on autonomous vehicle racing,” IEEE Open Journal of Intelligent Transportation Systems , vol. 3, pp. 458–488, 2022
2022
Later among the works it cites.
H. Karnan, K. S. Sikand, P. Atreya, S. Rabiee, X. Xiao, G. Warnell, P. Stone, and J. Biswas, “VI-IKD: High-speed accurate off-road navigation using learned visual-inertial inverse kinodynamics,” in Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2022, pp. 3294–3301
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
L. Wellhausen, A. Dosovitskiy, R. Ranftl, K. Walas, C. Cadena, and M. Hutter, “Where should I walk? predicting terrain properties from images via self-supervised learning,” IEEE Robotics and Automation Letters , vol. 4, no. 2, pp. 1509–1516, 2019
2019
Cited alongside, same era.
J. Kabzan, L. Hewing, A. Liniger, and M. N. Zeilinger, “Learning-based model predictive control for autonomous racing,” IEEE Robotics and Automation Letters , vol. 4, no. 4, pp. 3363–3370, 2019
2019
Cited alongside, same era.
N. A. Spielberg, M. Brown, N. R. Kapania, J. C. Kegelman, and J. C. Gerdes, “Neural network vehicle models for high-performance automated driving,” Science robotics , vol. 4, no. 28, p. eaaw1975, 2019
2019
Cited alongside, same era.
R. Sonker and A. Dutta, “Adding terrain height to improve model learning for path tracking on uneven terrain by a four wheel robot,” IEEE Robotics and Automation Letters , vol. 6, no. 1, pp. 239–246, 2020
2020
Cited alongside, same era.
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
Cited alongside, same era.
S. J. Wang, S. Triest, W. Wang, S. Scherer, and A. Johnson, “Rough terrain navigation using divergence constrained model-based reinforcement learning,” in Proc. Conference on Robot Learning (CoRL) , 2021, pp. 224–233
2021
Cited alongside, same era.
2022
Later among the works it cites.
S. Triest, M. Sivaprakasam, S. J. Wang, W. Wang, A. M. Johnson, and S. Scherer, “TartanDrive: A large-scale dataset for learning off-road dynamics models,” in Proc. IEEE International Conference on Robotics and Automation (ICRA) , 2022, pp. 2546–2552
2022
Later among the works it cites.
A. J. Sathyamoorthy, K. Weerakoon, T. Guan, J. Liang, and D. Manocha, “TerraPN: Unstructured terrain navigation using online self-supervised learning,” in Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2022, pp. 7197–7204
2022
Later among the works it cites.
T. Miki, J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “Learning robust perceptive locomotion for quadrupedal robots in the wild,” Science Robotics , vol. 7, no. 62, p. eabk2822, 2022
2022
Later among the works it cites.
T. Kim, G. Park, K. Kwak, J. Bae, and W. Lee, “Smooth model predictive path integral control without smoothing,” IEEE Robotics and Automation Letters , vol. 7, no. 4, pp. 10 406–10 413, 2022
2022
Later among the works it cites.
J. Seo, T. Kim, K. Kwak, J. Min, and I. Shim, “ScaTE: A scalable framework for self-supervised traversability estimation in unstructured environments,” IEEE Robotics and Automation Letters , vol. 8, no. 2, pp. 888–895, 2023
2023
Closest in time.
T. Kim, J. Mun, J. Seo, B. Kim, and S. Hong, “Bridging active exploration and uncertainty-aware deployment using probabilistic ensemble neural network dynamics,” in Proc. Robotics: Science and Systems (RSS) , 2023
2023
Closest in time.