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Simulation has the potential to transform the development of robust algorithms for mobile agents deployed in safety-critical scenarios.
B. Wymann, E. Espié, C. Guionneau, C. Dimitrakakis, R. Coulom, and A. Sumner, “Torcs, the open racing car simulator,” Software available at http://torcs. sourceforge. net , vol. 4, no. 6, p. 2, 2000
2000
Earlier work this paper cites.
E. Todorov, T. Erez, and Y. Tassa, “MuJoCo: A physics engine for model-based control,” in Proceedings of the International Conference on Intelligent Robots and Systems (IROS) , 2012
2012
Earlier work this paper cites.
S. Levine and V. Koltun, “Guided policy search,” in International conference on machine learning . PMLR, 2013, pp. 1–9
2013
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.
2016
Earlier work this paper cites.
A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “CARLA: An Open Urban Driving Simulator,” in CoRL , 2017
2017
Earlier work this paper cites.
S. Shah, D. Dey, C. Lovett, and A. Kapoor, “AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles,” in FSR , 2017
2017
Earlier work this paper cites.
C. Godard, O. Mac Aodha, and G. J. Brostow, “Unsupervised monocular depth estimation with left-right consistency,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2017
2017
Earlier work this paper cites.
S. Shah, D. Dey, C. Lovett, and A. Kapoor, “AirSim: High-fidelity visual and physical simulation for autonomous vehicles,” in Field and Service Robotics (FSR) , 2017
2017
Earlier work this paper cites.
H. Xu, Y. Gao, F. Yu, and T. Darrell, “End-to-End Learning of Driving Models from Large-Scale Video Datasets,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2017
2017
Earlier work this paper cites.
X. Pan, Y. You, Z. Wang, and C. Lu, “Virtual to real reinforcement learning for autonomous driving,” 2017
2017
Earlier work this paper cites.
E. Mueggler, H. Rebecq, G. Gallego, T. Delbruck, and D. Scaramuzza, “The event-camera dataset and simulator: Event-based data for pose estimation, visual odometry, and slam,” The International Journal of Robotics Research , vol. 36, no. 2, pp. 142–149, 2017
2017
Earlier work this paper cites.
G. Gallego, J. E. Lund, E. Mueggler, H. Rebecq, T. Delbruck, and D. Scaramuzza, “Event-based, 6-dof camera tracking from photometric depth maps,” IEEE transactions on pattern analysis and machine intelligence , vol. 40, no. 10, pp. 2402–2412, 2017
2017
Earlier work this paper cites.
A. I. Maqueda, A. Loquercio, G. Gallego, N. García, and D. Scaramuzza, “Event-based vision meets deep learning on steering prediction for self-driving cars,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 5419–5427
2018
Earlier work this paper cites.
H. Fu, M. Gong, C. Wang, K. Batmanghelich, and D. Tao, “Deep ordinal regression network for monocular depth estimation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2018
2018
Earlier work this paper cites.
H. Rebecq, D. Gehrig, and D. Scaramuzza, “Esim: an open event camera simulator,” in Conference on Robot Learning . PMLR, 2018, pp. 969–982
2018
Earlier work this paper cites.
M. Müller, V. Casser, J. Lahoud, N. Smith, and B. Ghanem, “Sim4cv: A photo-realistic simulator for computer vision applications,” International Journal of Computer Vision , vol. 126, no. 9, pp. 902–919, 2018
2018
Earlier work this paper cites.
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 Conference on Computer Vision and Pattern Recognition (CVPR) , 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
F. Codevilla, M. Miiller, A. López, V. Koltun, and A. Dosovitskiy, “End-to-End Driving via Conditional Imitation Learning,” in IEEE International Conference on Robotics and Automation (ICRA) , 2018
2018
Cited alongside, same era.
F. Codevilla, A. M. Lopez, V. Koltun, and A. Dosovitskiy, “On offline evaluation of vision-based driving models,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 236–251
2018
Cited alongside, same era.
Y. Tassa, S. Tunyasuvunakool, A. Muldal, Y. Doron, S. Liu, S. Bohez, J. Merel, T. Erez, T. Lillicrap, and N. Heess, “dm_control: Software and tasks for continuous control,” arXiv preprint: 2006.12983 , 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
S. Manivasagam, S. Wang, K. Wong, W. Zeng, M. Sazanovich, S. Tan, B. Yang, W.-C. Ma, and R. Urtasun, “Lidarsim: Realistic lidar simulation by leveraging the real world,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 167–11 176
2020
Later among the works it cites.
T.-H. Wang, S. Manivasagam, M. Liang, B. Yang, W. Zeng, and R. Urtasun, “V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2020
2020
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A. Amini, L. Paull, T. Balch, S. Karaman, and D. Rus, “Learning Steering Bounds for Parallel Autonomous Systems,” in IEEE International Conference on Robotics and Automation (ICRA) , 2018
2018
Cited alongside, same era.
A. Amini, W. Schwarting, G. Rosman, B. Araki, S. Karaman, and D. Rus, “Variational autoencoder for end-to-end control of autonomous driving with novelty detection and training de-biasing,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2018, pp. 568–575
2018
Cited alongside, same era.
H. Jiang, D. Sun, V. Jampani, M.-H. Yang, E. Learned-Miller, and J. Kautz, “Super slomo: High quality estimation of multiple intermediate frames for video interpolation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 9000–9008
2018
Cited alongside, same era.
2019
Cited alongside, same era.
Y. Xu, X. Zhu, J. Shi, G. Zhang, H. Bao, and H. Li, “Depth completion from sparse lidar data with depth-normal constraints,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , October 2019
2019
Cited alongside, same era.
H. Rebecq, R. Ranftl, V. Koltun, and D. Scaramuzza, “Events-to-video: Bringing modern computer vision to event cameras,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2019
2019
Cited alongside, same era.
R. Tedrake and the Drake Development Team, “Drake: Model-based design and verification for robotics,” 2019. [Online]. Available: https://drake.mit.edu
2019
Cited alongside, same era.
M. Savva, A. Kadian, O. Maksymets, Y. Zhao, E. Wijmans, B. Jain, J. Straub, J. Liu, V. Koltun, J. Malik, D. Parikh, and D. Batra, “Habitat: A Platform for Embodied AI Research,” in Proceedings of the International Conference on Computer Vision (ICCV) , 2019
2019
Cited alongside, same era.
Later among the works it cites.
M. Lechner, R. Hasani, A. Amini, T. A. Henzinger, D. Rus, and R. Grosu, “Neural circuit policies enabling auditable autonomy,” Nature Machine Intelligence , vol. 2, no. 10, pp. 642–652, 2020
2020
Later among the works it cites.
J. Hawke, R. Shen, C. Gurau, S. Sharma, D. Reda, N. Nikolov, P. Mazur, S. Micklethwaite, N. Griffiths, A. Shah, and A. Kendall, “Urban Driving with Conditional Imitation Learning,” in IEEE International Conference on Robotics and Automation (ICRA) , 2020
2020
Later among the works it cites.
D. Chen, B. Zhou, V. Koltun, and P. Krähenbühl, “Learning by cheating,” in Conference on Robot Learning . PMLR, 2020, pp. 66–75
2020
Later among the works it cites.
D. Gehrig, M. Gehrig, J. Hidalgo-Carrió, and D. Scaramuzza, “Video to events: Recycling video datasets for event cameras,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 3586–3595
2020
Later among the works it cites.
H. Tang, Z. Liu, S. Zhao, Y. Lin, J. Lin, H. Wang, and S. Han, “Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution,” in European Conference on Computer Vision (ECCV) , 2020
2020
Later among the works it cites.
Y. Zhao, L. Bai, Z. Zhang, and X. Huang, “A surface geometry model for lidar depth completion,” IEEE Robotics and Automation Letters , vol. 6, no. 3, pp. 4457–4464, 2021
2021
Closest in time.
S. Tulyakov, D. Gehrig, S. Georgoulis, J. Erbach, M. Gehrig, Y. Li, and D. Scaramuzza, “Time lens: Event-based video frame interpolation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2021, pp. 16 155–16 164
2021
Closest in time.
D. Gehrig, M. Rüegg, M. Gehrig, J. Hidalgo-Carrió, and D. Scaramuzza, “Combining events and frames using recurrent asynchronous multimodal networks for monocular depth prediction,” IEEE Robotics and Automation Letters , vol. 6, no. 2, 2021
2021
Closest in time.
F. Paredes-Valles and G. C. H. E. de Croon, “Back to event basics: Self-supervised learning of image reconstruction for event cameras via photometric constancy,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2021, pp. 3446–3455
2021
Closest in time.
E. Coumans and Y. Bai, “Pybullet, a python module for physics simulation for games, robotics and machine learning,” http://pybullet.org, 2016–2021
2021
Closest in time.
F. Fuchs, Y. Song, E. Kaufmann, D. Scaramuzza, and P. Dürr, “Super-human performance in gran turismo sport using deep reinforcement learning,” IEEE Robotics and Automation Letters , vol. 6, no. 3, pp. 4257–4264, 2021
2021
Closest in time.