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>},}
