@misc{GuWangEtAl2026,
  author = {Gu, Y. and Wang, Y. and Chen, Y. and You, Y. and Luo, W. and Wang, Y. and Ding, W. and Li, B. and Yang, H. and Ivanovic, B. and Pavone, M.},
  title = {Accelerating Structured Chain-of-Thought in Autonomous Vehicles},
  booktitle = {{arxiv:2602.02864}},
  month = feb,
  year = {2026},
  url = {https://arxiv.org/abs/2602.02864}
}

@misc{GanZhuEtAl2025,
  author = {Gan, Y. and Zhu, L. and Shan, D. and Shi, B. and Yin, H. and Ivanovic, B. and Han, S. and Darrell, T. and Malik, J. and Pavone, M. and Li, B.},
  title = {FoundationMotion: Auto-Labeling and Reasoning about Spatial Movement in Videos},
  booktitle = {{arxiv:2512.10927}},
  month = dec,
  year = {2025},
  url = {https://arxiv.org/abs/2512.10927}
}

@misc{YangChenEtAl2025,
  author = {Yang, J. and Chen, Z. and You, Y. and Wang, Y. and Li, Y. and Chen, Y. and Li, B. and Ivanovic, B. and Pavone, M. and Wang, Y.},
  title = {Towards Efficient and Effective Multi-Camera Encoding for End-to-End Driving},
  booktitle = {{arxiv:2512.10947}},
  month = dec,
  year = {2025},
  url = {https://arxiv.org/abs/2512.10947}
}

@misc{CongLiangEtAl2025,
  author = {Cong, W. and Liang, Y. and Zhang, Y. and Yang, Z. and Wang, Y. and Ivanovic, B. and Pavone, M. and Chen, C. and Wang, Z. and Fan, Z.},
  title = {E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models},
  booktitle = {{arxiv:2506.01933}},
  month = jun,
  year = {2025},
  url = {https://arxiv.org/abs/2506.01933}
}

@misc{ZhaoIvanovicEtAl2024,
  author = {Zhao, H. and Ivanovic, B. and Mehr, N.},
  title = {Distributed NeRF Learning for Collaborative Multi-Robot Perception},
  booktitle = {{arxiv:2409.20289}},
  month = sep,
  year = {2024},
  url = {https://arxiv.org/abs/2409.20289}
}

@misc{LiZhuEtAl2024,
  author = {Li, B. and Zhu, L. and Tian, R. and Tan, S. and Chen, Y. and Lu, Y. and Cui, Y. and Veer, S. and Ehrlich, M. and Philion, J. and Weng, X. and Xue, F. and Fan, L. and Zhu, Y. and Kautz, J. and Tao, A. and Liu, M. and Fidler, S. and Ivanovic, B. and Darrell, T. and Malik, J. and Han, S. and Pavone, M.},
  title = {WOLF: Captioning Everything with a World Summarization Framework},
  booktitle = {{arxiv:2407.18908}},
  month = jul,
  year = {2024},
  url = {https://arxiv.org/abs/2407.18908}
}

@misc{FanWangEtAl2024,
  author = {Fan, Z. and Wang, P. and Zhao, Y. and Zhao, Y. and Ivanovic, B. and Wang, Z. and Pavone, M. and Yang, H. F.},
  title = {Learning Traffic Crashes as Language: Datasets, Benchmarks, and What-If Causal Analyses},
  booktitle = {{arxiv:2406.10789}},
  month = jun,
  year = {2024},
  url = {https://arxiv.org/abs/2406.10789}
}

@misc{FanCongEtAl2024,
  author = {Fan, Z. and Cong, W. and Wen, K. and Wang, K. and Zhang, J. and Ding, X. and Xu, D. and Ivanovic, B. and Pavone, M. and Pavlakos, G. and Wang, Z. and Wang, Y.},
  title = {InstantSplat: Sparse-View Gaussian Splatting in Seconds},
  booktitle = {{arxiv:2403.20309}},
  month = mar,
  year = {2024},
  url = {https://arxiv.org/abs/2403.20309}
}

@Inproceedings{CaoHallgartenEtAl2025,
  author = {Cao, W. and Hallgarten, M. and Li, T. and Dauner, D. and Gu, X. and Wang, C. and Miron, Y. and Aiello, M. and Li, H. and Gilitschenski, I. and Ivanovic, B. and Pavone, M. and Geiger, A. and Chitta, K.},
  title = {Pseudo-Simulation for Autonomous Driving},
  booktitle = {{Conference on Robot Learning (CoRL)}},
  month = nov,
  year = {2025},
  url = {https://arxiv.org/abs/2506.04218}
}

@Inproceedings{HanJiaEtAl2025,
  author = {Han, X. and Jia, Z. and Li, B. and Wang, Y. and Ivanovic, B. and You, Y. and Liu, L. and Wang, Y. and Pavone, M. and Feng, C. and Li, Y.},
  title = {Extrapolated Urban View Synthesis Benchmark},
  booktitle = {{IEEE/CVF International Conference on Computer Vision (ICCV)}},
  month = oct,
  year = {2025},
  url = {https://arxiv.org/abs/2412.05256}
}

@Inproceedings{HirotaHachiumaEtAl2025,
  author = {Hirota, Y. and Hachiuma, R. and Li, B. and Lu, X. and Boone, M. R. and Ivanovic, B. and Choi, Y. and Nakashima, Y.},
  title = {Bias in Gender Bias Benchmarks: How Spurious Features Distort Evaluation},
  booktitle = {{IEEE/CVF International Conference on Computer Vision (ICCV)}},
  month = oct,
  year = {2025}
}

@article{AlthoffIvanovicEtAl2025,
  author = {Althoff, T. and Ivanovic, B. and Hicks, J. L. and Delp, S. L. and King, A. C. and Leskovec, J.},
  title = {Countrywide Natural Experiment Links Built Environment to Physical Activity},
  journal = {{Nature}},
  volume = {645},
  number = {8080},
  pages = {407--413},
  month = sep,
  year = {2025},
  url = {https://arxiv.org/abs/2406.04557},
  puburl = {https://www.nature.com/articles/s41586-025-09321-3}
}

@Inproceedings{HirotaLiEtAl2025,
  author = {Hirota, Y. and Li, B. and Hachiuma, R. and Wu, Y-H. and Ivanovic, B. and Pavone, M. and Choi, Y. and Nakashima, Y.},
  title = {Lotus: A Leaderboard for Detailed Image Captioning from Quality to Societal Bias and User Preferences},
  booktitle = {{Proceedings of the Association for Computational Linguistics (ACL)}},
  month = jul,
  year = {2025}
}

@Inproceedings{ZhaoIvanovicEtAl2025b,
  author = {Zhao, H. and Ivanovic, B. and Mehr, N.},
  title = {RAMEN: Real-Time Asynchronous Multi-Agent Neural Implicit Mapping},
  booktitle = {{Robotics: Science and Systems (RSS)}},
  month = jul,
  year = {2025},
  url = {https://arxiv.org/abs/2502.19592}
}

@article{IvanovicSaltoriEtAl2025,
  author = {Ivanovic, B. and Saltori, C. and You, Y. and Wang, Y. and Luo, W. and Pavone, M.},
  title = {Efficient Multi-Camera Tokenization with Triplanes for End-to-End Driving},
  journal = {{IEEE Robotics and Automation Letters (RA-L)}},
  year = {2025},
  month = jun,
  url = {https://arxiv.org/abs/2506.12251}
}

@Inproceedings{NaumannGuEtAl2025,
  author = {Naumann, A. and Gu, X. and Dimlioglu, T. and Bojarski, M. and Degirmenci, A. and Popov, A. and Bisla, D. and Pavone, M. and Müller, U. and Ivanovic, B.},
  title = {Data Scaling Laws for End-to-End Autonomous Driving},
  booktitle = {{IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshop on Autonomous Driving (WAD)}},
  month = jun,
  year = {2025},
  url = {https://arxiv.org/abs/2504.04338}
}

@Inproceedings{ZhangKarkusEtAl2025,
  author = {Zhang, Z. and Karkus, P. and Igl, M. and Ding, W. and Chen, Y. and Ivanovic, B. and Pavone, M.},
  title = {Closed-Loop Supervised Fine-Tuning of Tokenized Traffic Models},
  booktitle = {{IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}},
  note = {Selected for Oral Presentation},
  month = jun,
  year = {2025},
  url = {https://arxiv.org/abs/2412.05334},
  puburl = {https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Closed-Loop_Supervised_Fine-Tuning_of_Tokenized_Traffic_Models_CVPR_2025_paper.html}
}

@Inproceedings{MaoLiEtAl2025,
  author = {Mao, J. and Li, B. and Ivanovic, B. and Chen, Y. and Wang, Y. and You, Y. and Xiao, C. and Xu, D. and Pavone, M. and Wang, Y.},
  title = {DreamDrive: Generative 4D Scene Modeling from Street View Images},
  booktitle = {{IEEE International Conference on Robotics and Automation (ICRA)}},
  month = may,
  year = {2025},
  address = {Atlanta, USA},
  url = {https://arxiv.org/abs/2501.00601},
  puburl = {https://ieeexplore.ieee.org/document/11127463}
}

@Inproceedings{HuangWengEtAl2025,
  author = {Huang, Z. and Weng, X. and Igl, M. and Chen, Y. and Cao, Y. and Ivanovic, B. and Pavone, M. and Lv, C.},
  title = {Gen-Drive: Enhancing Diffusion Generative Driving Policies with Reward Modeling and Reinforcement Learning Fine-Tuning},
  booktitle = {{IEEE International Conference on Robotics and Automation (ICRA)}},
  month = may,
  year = {2025},
  address = {Atlanta, USA},
  url = {https://arxiv.org/abs/2410.05582},
  puburl = {https://ieeexplore.ieee.org/document/11127286}
}

@Inproceedings{ChakrabortyFengEtAl2025,
  author = {Chakraborty, K. and Feng, Z. and Veer, S. and Sharma, A. and Ivanovic, B. and Pavone, M. and Bansal, S.},
  title = {System-Level Safety Monitoring and Recovery for Perception Failures in Autonomous Vehicles},
  booktitle = {{IEEE International Conference on Robotics and Automation (ICRA)}},
  month = may,
  year = {2025},
  address = {Atlanta, USA},
  url = {https://arxiv.org/abs/2409.17630}
}

@Inproceedings{YangHuangEtAl2025,
  author = {Yang, J. and Huang, J. and Chen, Y. and Wang, Y. and Li, B. and You, Y. and Sharma, A. and Igl, M. and Karkus, P. and Xu, D. and Ivanovic, B. and Wang, Y. and Pavone, M.},
  title = {STORM: Spatio-Temporal Reconstruction Model for Large-Scale Outdoor Scenes},
  booktitle = {{International Conference on Learning Representations (ICLR)}},
  month = may,
  year = {2025},
  url = {https://arxiv.org/abs/2501.00602}
}

@Inproceedings{ChenYangEtAl2024,
  author = {Chen, Z. and Yang, J. and Huang, J. and Lutio, R. d. and Esturo, J. M. and Ivanovic, B. and Litany, O. and Gojcic, Z. and Fidler, S. and Pavone, M. and Song, L. and Wang, Y.},
  title = {OmniRe: Omni Urban Scene Reconstruction},
  booktitle = {{International Conference on Learning Representations (ICLR)}},
  note = {Selected as Spotlight},
  month = may,
  year = {2025},
  url = {https://arxiv.org/abs/2408.16760}
}

@Inproceedings{ChoIvanovicEtAl2024,
  author = {Cho, J. H. and Ivanovic, B. and Cao, Y. and Schmerling, E. and Wang, Y. and Weng, X. and Li, B. and You, Y. and Krähenbühl, P. and Wang, Y. and Pavone, M.},
  title = {Language-Image Models with 3D Understanding},
  booktitle = {{International Conference on Learning Representations (ICLR)}},
  month = may,
  year = {2025},
  url = {https://arxiv.org/abs/2405.03685}
}

@Inproceedings{LuYangEtAl2024,
  author = {Lu, Z. and Yang, H. and Xu, D. and Li, B. and Ivanovic, B. and Pavone, M. and Wang, Y.},
  title = {LoRA3D: Low-Rank Self-Calibration of 3D Geometric Foundation Models},
  booktitle = {{International Conference on Learning Representations (ICLR)}},
  note = {Selected as Spotlight},
  month = may,
  year = {2025},
  url = {https://arxiv.org/abs/2412.07746}
}

@article{WangLavoieEtAl2025,
  author = {Wang, L. and Lavoie, M. and Papais, S. and Nisar, B. and Chen, Y. and Ding, W. and Ivanovic, B. and Shao, H. and Abuduweili, A. and Cook, E. and Zhou, Y. and Karkus, P. and Li, J. and Liu, C. and Pavone, M. and Waslander, S.},
  title = {Deployable and Generalizable Motion Prediction: Taxonomy, Open Challenges and Future Directions},
  journal = {{Foundations and Trends in Robotics}},
  year = {2025},
  url = {https://arxiv.org/abs/2505.09074}
}

@Inproceedings{FanZhangEtAl2024,
  author = {Fan, Z. and Zhang, J. and Cong, W. and Wang, P. and Li, R. and Wen, K. and Zhou, S. and Kadambi, A. and Wang, Z. and Xu, D. and Ivanovic, B. and Pavone, M. and Wang, Y.},
  title = {Large Spatial Model: End-to-End Unposed Images to Semantic 3D},
  booktitle = {{Advances in Neural Information Processing Systems (NeurIPS)}},
  month = dec,
  year = {2024},
  address = {Vancouver, Canada},
  url = {https://arxiv.org/abs/2410.18956},
  puburl = {https://proceedings.neurips.cc/paper_files/paper/2024/hash/46fd43174b82660f24e3ba11cf5e1340-Abstract-Conference.html}
}

@Inproceedings{DaunerHallgartenEtAl2024,
  author = {Dauner, D. and Hallgarten, M. and Li, T. and Weng, X. and Huang, Z. and Yang, Z. and Li, H. and Gilitschenski, I. and Ivanovic, B. and Pavone, M. and Geiger, A. and Chitta, K.},
  title = {NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking},
  booktitle = {{Advances in Neural Information Processing Systems (NeurIPS)}},
  month = dec,
  year = {2024},
  address = {Vancouver, Canada},
  url = {https://arxiv.org/abs/2406.15349},
  puburl = {https://proceedings.neurips.cc/paper_files/paper/2024/hash/32768f7faf1995026ef9821c696f3404-Abstract-Datasets_and_Benchmarks_Track.html}
}

@Inproceedings{WangKimEtAl2024,
  author = {Wang, L. and Kim, S. W. and Yang, J. and Yu, C. and Ivanovic, B. and Waslander, S. L. and Wang, Y. and Fidler, S. and Pavone, M. and Karkus, P.},
  title = {DistillNeRF: Perceiving 3D Scenes from Single-Glance Images by Distilling Neural Fields and Foundation Model Features},
  booktitle = {{Advances in Neural Information Processing Systems (NeurIPS)}},
  month = dec,
  year = {2024},
  address = {Vancouver, Canada},
  url = {https://arxiv.org/abs/2406.12095},
  puburl = {https://proceedings.neurips.cc/paper_files/paper/2024/hash/720991812855c99df50bc8b36966cd81-Abstract-Conference.html}
}

@Inproceedings{DeglurkarShenEtAl2024,
  author = {Deglurkar, S. and Shen, H. and Muthali, A. and Pavone, M. and Margineantu, D. and Karkus, P. and Ivanovic, B. and Tomlin, C. J.},
  title = {System-Level Analysis of Module Uncertainty Quantification in the Autonomy Pipeline},
  booktitle = {{IEEE Conference on Decision and Control (CDC)}},
  month = dec,
  year = {2024},
  url = {https://arxiv.org/abs/2410.12019}
}

@Inproceedings{TanIvanovicEtAl2024,
  author = {Tan, S. and Ivanovic, B. and Chen, Y. and Li, B. and Weng, X. and Cao, Y. and Krähenbühl, P. and Pavone, M.},
  title = {Promptable Closed-Loop Traffic Simulation},
  booktitle = {{Conference on Robot Learning (CoRL)}},
  month = nov,
  year = {2024},
  url = {https://arxiv.org/abs/2409.05863}
}

@Inproceedings{TianLiEtAl2024,
  author = {Tian, R. and Li, B. and Weng, X. and Chen, Y. and Schmerling, E. and Wang, Y. and Ivanovic, B. and Pavone, M.},
  title = {Tokenize the World into Object-Level Knowledge to Address Long-Tail Events in Autonomous Driving},
  booktitle = {{Conference on Robot Learning (CoRL)}},
  month = nov,
  year = {2024},
  url = {https://arxiv.org/abs/2407.00959}
}

@Inproceedings{GuSongEtAl2024,
  author = {Gu, X. and Song, G. and Gilitschenski, I. and Pavone, M. and Ivanovic, B.},
  title = {Accelerating Online Mapping and Behavior Prediction via Direct BEV Feature Attention},
  booktitle = {{European Conference on Computer Vision (ECCV)}},
  month = oct,
  year = {2024},
  address = {Milan, Italy},
  url = {https://arxiv.org/abs/2407.06683},
  puburl = {https://www.ecva.net/papers/eccv_2024/papers_ECCV/html/2871_ECCV_2024_paper.php}
}

@Inproceedings{SinghWangEtAl2024,
  author = {Singh, G. and Wang, Y. and Yang, J. and Ivanovic, B. and Ahn, S. and Pavone, M. and Che, T.},
  title = {Parallelized Spatiotemporal Slot Binding for Videos},
  booktitle = {{International Conference on Machine Learning (ICML)}},
  month = jul,
  year = {2024},
  address = {Vienna, Austria},
  url = {https://arxiv.org/abs/2402.17077}
}

@Inproceedings{WengIvanovicEtAl2024,
  author = {Weng, X. and Ivanovic, B. and Wang, Y. and Wang, Y. and Pavone, M.},
  title = {Para-Drive: Parallelized Architecture for Real-Time Autonomous Driving},
  booktitle = {{IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}},
  month = jun,
  year = {2024},
  address = {Seattle, USA},
  url = {https://xinshuoweng.github.io/paradrive/},
  puburl = {https://openaccess.thecvf.com/content/CVPR2024/html/Weng_PARA-Drive_Parallelized_Architecture_for_Real-time_Autonomous_Driving_CVPR_2024_paper.html}
}

@Inproceedings{GuSongEtAl2024b,
  author = {Gu, X. and Song, G. and Gilitschenski, I. and Pavone, M. and Ivanovic, B.},
  title = {Producing and Leveraging Online Map Uncertainty in Trajectory Prediction},
  booktitle = {{IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}},
  note = {Best Paper Award Finalist},
  month = jun,
  year = {2024},
  address = {Seattle, USA},
  url = {https://arxiv.org/abs/2403.16439},
  puburl = {https://openaccess.thecvf.com/content/CVPR2024/html/Gu_Producing_and_Leveraging_Online_Map_Uncertainty_in_Trajectory_Prediction_CVPR_2024_paper.html}
}

@Inproceedings{LiWangEtAl2024,
  author = {Li, B. and Wang, Y. and Mao, J. and Ivanovic, B. and Veer, S. and Leung, K. and Pavone, M.},
  title = {Driving Everywhere with Large Language Model Policy Adaptation},
  booktitle = {{IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}},
  month = jun,
  year = {2024},
  address = {Seattle, USA},
  url = {https://arxiv.org/abs/2402.05932},
  puburl = {https://openaccess.thecvf.com/content/CVPR2024/html/Li_Driving_Everywhere_with_Large_Language_Model_Policy_Adaptation_CVPR_2024_paper.html}
}

@Inproceedings{HuangKarkusEtAl2024,
  author = {Huang, Z. and Karkus, P. and Ivanovic, B. and Chen, Y. and Pavone, M. and Lv, C.},
  title = {DTPP: Differentiable Joint Conditional Prediction and Cost Evaluation for Tree Policy Planning in Autonomous Driving},
  booktitle = {{IEEE International Conference on Robotics and Automation (ICRA)}},
  month = may,
  year = {2024},
  address = {Yokohama, Japan},
  url = {https://arxiv.org/abs/2310.05885},
  puburl = {https://ieeexplore.ieee.org/document/10610550}
}

@Inproceedings{CaoIvanovicEtAl2024,
  author = {Cao, Y. and Ivanovic, B. and Xiao, C. and Pavone, M.},
  title = {Reinforcement Learning with Human Feedback for Realistic Traffic Simulation},
  booktitle = {{IEEE International Conference on Robotics and Automation (ICRA)}},
  month = may,
  year = {2024},
  address = {Yokohama, Japan},
  url = {https://arxiv.org/abs/2309.00709},
  puburl = {https://ieeexplore.ieee.org/document/10610878}
}

@Inproceedings{YangIvanovicEtAl2024,
  author = {Yang, J. and Ivanovic, B. and Litany, O. and Weng, X. and Kim, S. W. and Li, B. and Che, T. and Xu, D. and Fidler, S. and Pavone, M. and Wang, Y.},
  title = {EmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-Supervision},
  booktitle = {{International Conference on Learning Representations (ICLR)}},
  month = may,
  year = {2024},
  address = {Vienna, Austria},
  url = {https://arxiv.org/abs/2311.02077},
  puburl = {https://openreview.net/forum?id=ycv2z8TYur}
}

@Inproceedings{FanYunTremblayEtAl2023,
  author = {Sun, F. and Tremblay, J. and Blukis, V. and Lin, K. and Xu, D. and Ivanovic, B. and Karkus, P. and Birchfield, S. and Fox, D. and Zhang, R. and Li, Y. and Wu, J. and Pavone, M. and Haber, N.},
  title = {Partial-View Object View Synthesis via Filtered Inversion},
  booktitle = {{International Conference on 3D Vision (3DV)}},
  note = {Selected as Spotlight},
  month = mar,
  year = {2024},
  address = {Davos, Switzerland},
  url = {https://arxiv.org/abs/2304.00673},
  puburl = {https://ieeexplore.ieee.org/document/10550539}
}

@Inproceedings{IvanovicSongEtAl2023,
  author = {Ivanovic, B. and Song, G. and Gilitschenski, I. and Pavone, M.},
  title = {trajdata: A Unified Interface to Multiple Human Trajectory Datasets},
  booktitle = {{Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track}},
  month = dec,
  year = {2023},
  address = {New Orleans, USA},
  url = {https://arxiv.org/abs/2307.13924},
  code = {https://github.com/NVlabs/trajdata},
  puburl = {https://proceedings.neurips.cc/paper_files/paper/2023/hash/57bb67dbe17bfb660c8c63d089ea05b9-Abstract-Datasets_and_Benchmarks.html}
}

@Inproceedings{TanIvanovicEtAl2023,
  author = {Tan, S. and Ivanovic, B. and Weng, X. and Pavone, M. and Kraehenbuehl, P.},
  title = {Language Conditioned Traffic Generation},
  booktitle = {{Conference on Robot Learning (CoRL)}},
  month = nov,
  year = {2023},
  address = {Atlanta, USA},
  url = {https://arxiv.org/abs/2307.07947},
  code = {https://github.com/Ariostgx/lctgen},
  puburl = {https://proceedings.mlr.press/v229/tan23a.html}
}

@Inproceedings{ZhongRempeEtAl2023,
  author = {Zhong, Z. and Rempe, D. and Chen, Y. and Ivanovic, B. and Cao, Y. and Xu, D. and Pavone, M. and Ray, B.},
  title = {Language-Guided Traffic Simulation via Scene-Level Diffusion},
  booktitle = {{Conference on Robot Learning (CoRL)}},
  month = nov,
  year = {2023},
  address = {Atlanta, USA},
  note = {Selected for Oral Presentation},
  url = {https://arxiv.org/abs/2306.06344},
  puburl = {https://proceedings.mlr.press/v229/zhong23a.html}
}

@Inproceedings{IvanovicHarrisonEtAl2023,
  author = {Ivanovic, B. and Harrison, J. and Pavone, M.},
  title = {Expanding the Deployment Envelope of Behavior Prediction via Adaptive Meta-Learning},
  booktitle = {{IEEE International Conference on Robotics and Automation (ICRA)}},
  month = may,
  year = {2023},
  address = {London, UK},
  owner = {borisi},
  timestamp = {2023-01-18},
  url = {https://arxiv.org/abs/2209.11820},
  code = {https://github.com/NVlabs/adaptive-prediction},
  puburl = {https://ieeexplore.ieee.org/document/10160583}
}

@Inproceedings{XuChenEtAl2023,
  author = {Xu, D. and Chen, Y. and Ivanovic, B. and Pavone, M.},
  title = {{BITS}: Bi-level Imitation for Traffic Simulation},
  booktitle = {{IEEE International Conference on Robotics and Automation (ICRA)}},
  month = may,
  year = {2023},
  address = {London, UK},
  owner = {borisi},
  timestamp = {2023-01-18},
  url = {https://arxiv.org/abs/2208.12403},
  puburl = {https://ieeexplore.ieee.org/document/10161457}
}

@Inproceedings{ChenKarkusEtAl2023,
  author = {Chen, Y. and Karkus, P. and Ivanovic, B. and Weng, X. and Pavone, M.},
  title = {Tree-structured Policy Planning with Learned Behavior Models},
  booktitle = {{IEEE International Conference on Robotics and Automation (ICRA)}},
  month = may,
  year = {2023},
  address = {London, UK},
  owner = {borisi},
  timestamp = {2023-01-18},
  url = {https://arxiv.org/abs/2301.11902}
}

@Inproceedings{ChristianosKarkusEtAl2023,
  author = {Christianos, F. and Karkus, P. and Ivanovic, B. and Albrecht, S. V. and Pavone, M.},
  title = {Planning with Occluded Traffic Agents using Bi-Level Variational Occlusion Models},
  booktitle = {{IEEE International Conference on Robotics and Automation (ICRA)}},
  month = may,
  year = {2023},
  address = {London, UK},
  owner = {borisi},
  timestamp = {2023-01-18},
  url = {https://arxiv.org/abs/2210.14584}
}

@Inproceedings{ZhangCheEtAl2023,
  author = {Zhang, R. and Che, T. and Ivanovic, B. and Wang, R. and Pavone, M. and Bengio, Y. and Paull, L.},
  title = {Robust and Controllable Object-Centric Learning through Energy-based Models},
  booktitle = {{International Conference on Learning Representations (ICLR)}},
  month = may,
  year = {2023},
  address = {Kigali, Rwanda},
  owner = {borisi},
  timestamp = {2023-01-20},
  url = {https://arxiv.org/abs/2210.05519},
  puburl = {https://openreview.net/forum?id=wcNtbEtcGIC}
}

@Inproceedings{KarkusIvanovicEtAl2022,
  author = {Karkus, P. and Ivanovic, B. and Mannor, S. and Pavone, M.},
  title = {DiffStack: A Differentiable and Modular Control Stack for Autonomous Vehicles},
  booktitle = {{Conference on Robot Learning (CoRL)}},
  month = dec,
  year = {2022},
  address = {Auckland, New Zealand},
  owner = {borisi},
  timestamp = {2022-09-24},
  url = {https://openreview.net/pdf?id=teEnA3L4aRe},
  code = {https://github.com/NVlabs/diffstack},
  puburl = {https://proceedings.mlr.press/v205/karkus23a.html}
}

@Inproceedings{FaridVeerEtAl2022,
  author = {Farid, A. and Veer, S. and Ivanovic, B. and Leung, K. and Pavone, M.},
  title = {Task-Relevant Failure Detection for Trajectory Predictors in Autonomous Vehicles},
  booktitle = {{Conference on Robot Learning (CoRL)}},
  month = dec,
  year = {2022},
  address = {Auckland, New Zealand},
  owner = {borisi},
  timestamp = {2022-09-24},
  url = {https://openreview.net/forum?id=oPRhm0Aben_},
  puburl = {https://proceedings.mlr.press/v205/farid23a.html}
}

@Inproceedings{IvanovicLeeEtAl2022,
  author = {Ivanovic, B. and Lee, K. and Tokmakov, P. and Wulfe, B. and McAllister, R. and Gaidon, A. and Pavone, M.},
  title = {Heterogeneous-Agent Trajectory Forecasting Incorporating Class Uncertainty},
  booktitle = {{IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}},
  month = oct,
  year = {2022},
  address = {Kyoto, Japan},
  owner = {borisi},
  timestamp = {2021-10-12},
  url = {https://arxiv.org/abs/2104.12446},
  puburl = {https://ieeexplore.ieee.org/document/9981095}
}

@Inproceedings{IvanovicPavone2022,
  author = {Ivanovic, B. and Pavone, M.},
  title = {Injecting Planning-Awareness into Prediction and Detection Evaluation},
  month = jun,
  year = {2022},
  address = {Aachen, Germany},
  booktitle = {{IEEE Intelligent Vehicles Symposium (IV)}},
  owner = {borisi},
  timestamp = {2021-10-12},
  url = {https://arxiv.org/abs/2110.03270},
  code = {https://github.com/BorisIvanovic/PlanningAwareEvaluation},
  puburl = {https://ieeexplore.ieee.org/document/9827101}
}

@Inproceedings{WengIvanovicEtAl2022a,
  author = {Weng, X. and Ivanovic, B. and Pavone, M.},
  title = {MTP: Multi-hypothesis Tracking and Prediction for Reduced Error Propagation},
  month = jun,
  year = {2022},
  address = {Aachen, Germany},
  booktitle = {{IEEE Intelligent Vehicles Symposium (IV)}},
  owner = {borisi},
  timestamp = {2021-10-12},
  url = {https://arxiv.org/abs/2110.09481},
  puburl = {https://ieeexplore.ieee.org/document/9827315}
}

@Inproceedings{LuoZhaoEtAl2022,
  author = {Luo, R. and Zhao, S. and Kuck, J. and Ivanovic, B. and Savarese, S. and Schmerling, E. and Pavone, M.},
  title = {Sample-Efficient Safety Assurances using Conformal Prediction},
  month = jun,
  year = {2022},
  address = {College Park, MD, USA},
  booktitle = {{Workshop on the Algorithmic Foundations of Robotics (WAFR)}},
  owner = {borisi},
  timestamp = {2021-10-12},
  url = {https://arxiv.org/abs/2109.14082}
}

@Inproceedings{WengIvanovicEtAl2022b,
  author = {Weng, X. and Ivanovic, B. and Pavone, M.},
  title = {Whose Track Is It Anyway? Improving Robustness to Tracking Errors with Affinity-Based Prediction},
  booktitle = {{IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}},
  month = jun,
  year = {2022},
  address = {New Orleans, USA},
  owner = {borisi},
  timestamp = {2022-03-25},
  url = {},
  puburl = {https://openaccess.thecvf.com/content/CVPR2022/html/Weng_Whose_Track_Is_It_Anyway_Improving_Robustness_to_Tracking_Errors_CVPR_2022_paper.html}
}

@Inproceedings{ChenIvanovicEtAl2022,
  author = {Chen, Y. and Ivanovic, B. and Pavone, M.},
  title = {{ScePT}: Scene-consistent, Policy-based Trajectory Predictions for Planning},
  booktitle = {{IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}},
  month = jun,
  year = {2022},
  address = {New Orleans, USA},
  owner = {borisi},
  timestamp = {2022-03-25},
  url = {},
  code = {https://github.com/NVlabs/ScePT},
  puburl = {https://openaccess.thecvf.com/content/CVPR2022/html/Chen_ScePT_Scene-Consistent_Policy-Based_Trajectory_Predictions_for_Planning_CVPR_2022_paper.html}
}

@Inproceedings{IvanovicLinEtAl2022,
  author = {Ivanovic, B. and Lin, Y. and Shrivastava, S. and Chakravarty, P. and Pavone, M.},
  title = {Propagating State Uncertainty Through Trajectory Forecasting},
  booktitle = {{IEEE International Conference on Robotics and Automation (ICRA)}},
  month = may,
  year = {2022},
  address = {Philadelphia, USA},
  owner = {borisi},
  timestamp = {2021-10-12},
  url = {https://arxiv.org/abs/2110.03267},
  code = {https://github.com/StanfordASL/PSU-TF},
  puburl = {https://ieeexplore.ieee.org/document/9811776}
}

@Inproceedings{SchaeferLeungEtAl2021,
  author = {Schaefer, S. and Leung, K. and Ivanovic, B. and Pavone, M.},
  title = {Leveraging Neural Network Gradients within Trajectory Optimization for Proactive Human-Robot Interactions},
  booktitle = {{IEEE International Conference on Robotics and Automation (ICRA)}},
  year = {2021},
  address = {Xi'an, China},
  month = may,
  url = {https://arxiv.org/abs/2012.01027},
  owner = {borisi},
  timestamp = {2020-11-01},
  puburl = {https://ieeexplore.ieee.org/document/9561786}
}

@article{IvanovicLeungEtAl2020,
  author = {Ivanovic, B. and Leung, K. and Schmerling, E. and Pavone, M.},
  title = {Multimodal Deep Generative Models for Trajectory Prediction: A Conditional Variational Autoencoder Approach},
  journal = {{IEEE Robotics and Automation Letters (RA-L)}},
  volume = {6},
  number = {2},
  pages = {295--302},
  year = {2021},
  note = {In Press},
  month = apr,
  url = {https://arxiv.org/pdf/2008.03880.pdf},
  keywords = {press},
  puburl = {https://ieeexplore.ieee.org/document/9286651}
}

@Inproceedings{ItkinaIvanovicEtAl2020,
  author       = {Itkina, M. and Ivanovic, B. and Senanayake, R. and Kochenderfer, M. J. and Pavone, M.},
  title        = {Evidential Sparsification of Multimodal Latent Spaces in Conditional Variational Autoencoders},
  booktitle    = {{Advances in Neural Information Processing Systems (NeurIPS)}},
  year         = {2020},
  address = {Virtual},
  month    = {12},
  url      = {https://arxiv.org/pdf/2010.09164.pdf},
  puburl = {https://proceedings.neurips.cc/paper/2020/hash/73f95ee473881dea4afd89c06165fa66-Abstract.html}
}

@Inproceedings{IvanovicElhafsiEtAl2020,
  author       = {Ivanovic, B. and Elhafsi, A. and Rosman, G. and Gaidon, A. and Pavone, M.},
  title        = {MATS: An Interpretable Trajectory Forecasting Representation for Planning and Control},
  booktitle    = {{Conference on Robot Learning (CoRL)}},
  year         = {2020},
  address = {Virtual},
  month        = {11},
  abstract     = {Reasoning about human motion is a core component of modern human-robot interactive systems. In particular, one of the main uses of behavior prediction in autonomous systems is to inform ego-robot motion planning and control. However, a majority of planning and control algorithms reason about system dynamics rather than the predicted agent tracklets that are commonly output by trajectory forecasting methods, which can hinder their integration. Towards this end, we propose Mixtures of Affine Time-varying Systems (MATS) as an output representation for trajectory forecasting that is more amenable to downstream planning and control use. Our approach leverages successful ideas from probabilistic trajectory forecasting works to learn dynamical system representations that are well-studied in the planning and control literature. We integrate our predictions with a proposed multimodal planning methodology and demonstrate significant computational efficiency improvements on a large-scale autonomous driving dataset.},
  url          = {https://arxiv.org/pdf/2009.07517.pdf},
  code         = {https://github.com/StanfordASL/MATS},
  puburl = {https://proceedings.mlr.press/v155/ivanovic21a.html}
}

@Inproceedings{NishimuraIvanovicEtAl2020,
  author = {Nishimura, H. and Ivanovic, B. and Gaidon, A. and Pavone, M. and Schwager, M.},
  title = {Risk-Sensitive Sequential Action Control with Multi-Modal Human Trajectory Forecasting for Safe Crowd-Robot Interaction},
  booktitle = {{IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}},
  year = {2020},
  address = {Virtual},
  month = {10},
  url = {https://arxiv.org/pdf/2009.05702.pdf},
  puburl = {https://ieeexplore.ieee.org/document/9341137}
}

@Inproceedings{SalzmannIvanovicEtAl2020,
  author       = {Salzmann, T. and Ivanovic, B. and Chakravarty, P. and Pavone, M.},
  title        = {Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data},
  booktitle    = {{European Conference on Computer Vision (ECCV)}},
  year         = {2020},
  address = {Virtual},
  month        = {8},
  abstract     = {Reasoning about human motion is an important prerequisite to safe and socially-aware robotic navigation. As a result, multi-agent behavior prediction has become a core component of modern human-robot interactive systems, such as self-driving cars. While there exist many methods for trajectory forecasting, most do not enforce dynamic constraints and do not account for environmental information (e.g., maps). Towards this end, we present Trajectron++, a modular, graph-structured recurrent model that forecasts the trajectories of a general number of diverse agents while incorporating agent dynamics and heterogeneous data (e.g., semantic maps). Trajectron++ is designed to be tightly integrated with robotic planning and control frameworks; for example, it can produce predictions that are optionally conditioned on ego-agent motion plans. We demonstrate its performance on several challenging real-world trajectory forecasting datasets, outperforming a wide array of state-of-the-art deterministic and generative methods.},
  url          = {https://arxiv.org/pdf/2001.03093.pdf},
  note         = {3rd place in the ICRA 2020 nuScenes Prediction Challenge},
  code         = {https://github.com/StanfordASL/Trajectron-plus-plus},
  puburl = {https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3094_ECCV_2020_paper.php}
}

@Inproceedings{ElhafsiIvanovicEtAl2020,
  author       = {Elhafsi, A. and Ivanovic, B. and Janson, L. and Pavone, M.},
  title        = {Map-Predictive Motion Planning in Unknown Environments},
  booktitle    = {{IEEE International Conference on Robotics and Automation (ICRA)}},
  year         = {2020},
  address = {Virtual},
  month        = {6},
  abstract     = {Algorithms for motion planning in unknown environments are generally limited in their ability to reason about the structure of the unobserved environment. As such, current methods generally navigate unknown environments by relying on heuristic methods to choose intermediate objectives along frontiers. We present a unified method that combines map prediction and motion planning for safe, time-efficient autonomous navigation of unknown environments by dynamically-constrained robots. We propose a data-driven method for predicting the map of the unobserved environment, using the robot's observations of its surroundings as context. These map predictions are then used to plan trajectories from the robot's position to the goal without requiring frontier selection. We demonstrate that our map-predictive motion planning strategy yields a substantial improvement in trajectory time over a naive frontier pursuit method and demonstrates similar performance to methods using more sophisticated frontier selection heuristics with significantly shorter computation time.},
  url          = {https://arxiv.org/pdf/1910.08184.pdf},
  puburl = {https://ieeexplore.ieee.org/document/9196803}
}

@Inproceedings{IvanovicPavone2019,
  author       = {Ivanovic, B. and Pavone, M.},
  title        = {The {Trajectron}: Probabilistic Multi-Agent Trajectory Modeling with Dynamic Spatiotemporal Graphs},
  booktitle    = {{IEEE/CVF International Conference on Computer Vision (ICCV)}},
  year         = {2019},
  abstract = {Developing safe human-robot interaction systems is a necessary step towards the widespread integration of autonomous agents in society. A key component of such systems is the ability to reason about the many potential futures (e.g. trajectories) of other agents in the scene. Towards this end, we present the Trajectron, a graph-structured model that predicts many potential future trajectories of multiple agents simultaneously in both highly dynamic and multimodal scenarios (i.e. where the number of agents in the scene is time-varying and there are many possible highly-distinct futures for each agent). It combines tools from recurrent sequence modeling and variational deep generative modeling to produce a distribution of future trajectories for each agent in a scene. We demonstrate the performance of our model on several datasets, obtaining state-of-the-art results on standard trajectory prediction metrics as well as introducing a new metric for comparing models that output distributions.},
  address  = {Seoul, South Korea},
  month    = {10},
  url      = {https://arxiv.org/pdf/1810.05993.pdf},
  timestamp = {2019-07-22},
  code     = {https://github.com/StanfordASL/Trajectron},
  puburl = {https://openaccess.thecvf.com/content_ICCV_2019/html/Ivanovic_The_Trajectron_Probabilistic_Multi-Agent_Trajectory_Modeling_With_Dynamic_Spatiotemporal_Graphs_ICCV_2019_paper.html}
}

@inproceedings{IvanovicHarrisonEtAl2019,
  author = {Ivanovic, B. and Harrison, J. and Sharma, A. and Chen, M. and Pavone, M.},
  title = {{BaRC:} Backward Reachability Curriculum for Robotic Reinforcement Learning},
  booktitle = {{IEEE International Conference on Robotics and Automation (ICRA)}},
  year = {2019},
  address = {Montreal, Canada},
  month = {5},
  url = {https://arxiv.org/pdf/1806.06161.pdf},
  code = {https://github.com/StanfordASL/BaRC},
  puburl = {https://ieeexplore.ieee.org/document/8793742}
}

@Inproceedings{IvanovicEtAl2018,
  author       = {Ivanovic, B. and Schmerling, E. and Leung, K. and Pavone, M.},
  title        = {Generative Modeling of Multimodal Multi-Human Behavior},
  booktitle    = {{IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}},
  year         = {2018},
  abstract = {This work presents a methodology for modeling and predicting human behavior in settings with N humans interacting in highly multimodal scenarios (i.e. where there are many possible highly-distinct futures). A motivating example includes robots interacting with humans in crowded environments, such as self-driving cars operating alongside human-driven vehicles or human-robot collaborative bin packing in a warehouse. Our approach to model human behavior in such uncertain environments is to model humans in the scene as nodes in a graphical model, with edges encoding relationships between them. For each human, we learn a multimodal probability distribution over future actions from a dataset of multi-human interactions. Learning such distributions is made possible by recent advances in the theory of conditional variational autoencoders and deep learning approximations of probabilistic graphical models. Specifically, we learn action distributions conditioned on interaction history, neighboring human behavior, and candidate future agent behavior in order to take into account response dynamics. We demonstrate the performance of such a modeling approach in modeling basketball player trajectories, a highly multimodal, multi-human scenario which serves as a proxy for many robotic applications.},
  address  = {Madrid, Spain},
  month    = {3},
  url      = {https://arxiv.org/pdf/1803.02015.pdf},
  owner        = {borisi},
  timestamp    = {2018-03-06},
  code         = {https://github.com/StanfordASL/NHumanModeling},
  puburl = {https://ieeexplore.ieee.org/document/8594393}
}

@inproceedings{HarrisonEtAl2017,
author = {Harrison, J. and Garg, A. and Ivanovic, B. and Zhu, Y. and Savarese, S. and Fei-Fei, L. and Pavone, M.},
title = {{ADAPT:} Zero-Shot Adaptive Policy Transfer for Stochastic Dynamical Systems},
booktitle = {{International Symposium on Robotics Research (ISRR)}},
year = {2017},
address = {Puerto Varas, Chile},
month = {12},
url = {https://arxiv.org/pdf/1707.04674.pdf},
keywords = {Reinforcement Learning, Policy Transfer, Model-Predictive Control},
  puburl = {https://link.springer.com/chapter/10.1007/978-3-030-28619-4_50}
}