Our research focuses on enabling the safe deployment of learning-based autonomous systems interacting with humans. We study challenges across the deployment pipeline: evaluating systems before deployment, building adaptive autonomy for human environments, and integrating autonomous systems into infrastructure networks.
Evaluating Autonomous Systems Before Deployment
Scalable evaluation via generative simulation, diffusion models, and benchmarks
Adaptive Autonomy for Human Environments
Online policy customization via residual RL, imitation learning, and human-aware control
Autonomous Systems in Infrastructure Networks
Cooperative driving, language communication, and system-level optimization
Evaluating Autonomous Systems Before Deployment
Autonomous systems must operate safely in complex human environments, but real-world testing is limited and risky. We develop scalable evaluation methods — including generative simulation for interactive environments, diffusion models for trajectory prediction and scenario generation, and off-policy evaluation techniques — to rigorously assess autonomous system performance before deployment. We also build benchmarks and datasets for driving reasoning to enable standardized evaluation across the community.
Selected Publications
2025
ICML 2025
WOMD-Reasoning: A Large-Scale Language Dataset for Interactions and Driving Intentions Reasoning
Yiheng Li, Cunxin Fan, Chongjian Ge, and 9 more authors
Forty-Second International Conference on Machine Learning (ICML), 2025
@article{li2024womd,title={{WOMD-Reasoning}: A Large-Scale Language Dataset for Interactions and Driving Intentions Reasoning},author={Li, Yiheng and Fan, Cunxin and Ge, Chongjian and Zhao, Seth Z. and Li, Chenran and Xu, Chenfeng and Yao, Huaxiu and Tomizuka, Masayoshi and Zhou, Bolei and Tang, Chen and Ding, Mingyu and Zhan, Wei},journal={Forty-Second International Conference on Machine Learning (ICML)},year={2025},}
2024
ECCV 2024
Optimizing Diffusion Models for Joint Trajectory Prediction and Controllable Generation
Yixiao Wang, Chen Tang, Lingfeng Sun, and 8 more authors
In European Conference on Computer Vision (ECCV), 2024
@inproceedings{wang2024OGD,title={Optimizing Diffusion Models for Joint Trajectory Prediction and Controllable Generation},author={Wang, Yixiao and Tang, Chen and Sun, Lingfeng and Rossi, Simone and Xie, Yichen and Peng, Chensheng and Hannagan, Thomas and Sabatini, Stefano and Poerio, Nicola and Tomizuka, Masayoshi and Zhan, Wei},booktitle={European Conference on Computer Vision (ECCV)},year={2024},}
2021
NeurIPS 2021
Exploring Social Posterior Collapse in Variational Autoencoder for Interaction Modeling
Chen Tang, Wei Zhan, and Masayoshi Tomizuka
In Advances in Neural Information Processing Systems (NeurIPS), 2021
@inproceedings{tang2021social,author={Tang, Chen and Zhan, Wei and Tomizuka, Masayoshi},booktitle={Advances in Neural Information Processing Systems (NeurIPS)},pages={8481--8494},title={Exploring Social Posterior Collapse in Variational Autoencoder for Interaction Modeling},volume={34},year={2021},}
Adaptive Autonomy for Human Environments
Learning-based autonomous systems must adapt to human behavior, safety constraints, and changing objectives during deployment. We develop methods that combine residual reinforcement learning with control theory for online policy customization, enable offline-to-online RL for safe deployment, and leverage imitation learning from human demonstrations. Our work also explores how foundation models can operate under real-time control constraints to produce robust, human-aware autonomy.
Selected Publications
2025
ICLR 2025
Residual-MPPI: Online Policy Customization for Continuous Control
Pengcheng Wang*, Chenran Li*, Catherine Weaver, and 4 more authors
In International Conference on Learning Representations (ICLR), 2025
@inproceedings{wang2024residual,title={{Residual-MPPI}: Online Policy Customization for Continuous Control},author={Wang, Pengcheng and Li, Chenran and Weaver, Catherine and Kawamoto, Kenta and Tomizuka, Masayoshi and Tang, Chen and Zhan, Wei},booktitle={International Conference on Learning Representations (ICLR)},year={2025},}
CoRL 2025
MEReQ: Max-Ent Residual-Q Inverse RL for Sample-Efficient Alignment from Human Intervetion
Yuxin Chen*, Chen Tang*, Chenran Li, and 4 more authors
@article{chen2024mereq,title={{MEReQ}: Max-Ent Residual-Q Inverse RL for Sample-Efficient Alignment from Human Intervetion},author={Chen, Yuxin and Tang, Chen and Li, Chenran and Tian, Ran and Stone, Peter and Tomizuka, Masayoshi and Zhan, Wei},journal={Conference on Robot Learning (CoRL)},year={2025},}
2023
NeurIPS 2023
Residual Q-Learning: Offline and Online Policy Customization without Value
Chenran Li*, Chen Tang*, Haruki Nishimura, and 3 more authors
In Advances in Neural Information Processing Systems (NeurIPS), 2023
@inproceedings{tang2023residual,title={Residual Q-Learning: Offline and Online Policy Customization without Value},author={Li, Chenran and Tang, Chen and Nishimura, Haruki and Mercat, Jean and Tomizuka, Masayoshi and Zhan, Wei},booktitle={Advances in Neural Information Processing Systems (NeurIPS)},year={2023},note={<strong>featured in Nikkei Robotics</strong>},}
Autonomous Systems in Infrastructure Networks
When autonomous systems deploy at scale, they interact with large-scale transportation and infrastructure systems. We study how autonomous agents affect and integrate into these networks through cooperative autonomous driving via language communication, infrastructure-aware autonomy, and system-level optimization for safety and efficiency in human-AI mobility systems.
Selected Publications
2020
IEEE IV 2020
Application Specific System Identification for Model-Based Control in Self-Driving Cars
Julian M. Salt Ducaju, Chen Tang, and Masayoshi Tomizuka
In 2020 IEEE Intelligent Vehicles Symposium (IV), 2020
@inproceedings{ducaju2020application,title={Application Specific System Identification for Model-Based Control in Self-Driving Cars},author={Ducaju, Julian M. Salt and Tang, Chen and Tomizuka, Masayoshi},booktitle={2020 IEEE Intelligent Vehicles Symposium (IV)},year={2020},organization={IEEE},}
2018
IEEE IV 2018
Continuous Decision Making for On-Road Autonomous Driving under Uncertain and Interactive Environments
Jianyu Chen, Chen Tang, Long Xin, and 2 more authors
In 2018 IEEE Intelligent Vehicles Symposium (IV), 2018
@inproceedings{chen2018continuous,title={Continuous Decision Making for On-Road Autonomous Driving under Uncertain and Interactive Environments},author={Chen, Jianyu and Tang, Chen and Xin, Long and Li, Shengbo Eben and Tomizuka, Masayoshi},booktitle={2018 IEEE Intelligent Vehicles Symposium (IV)},pages={1651--1658},year={2018},organization={IEEE},}
For a complete list of publications, visit the Publications page.
