@article{tang2020gri,title={Grounded Relational Inference: Domain Knowledge-driven Explainable Autonomous Driving},author={Tang, Chen and Srishankar, Nishan and Martin, Sujitha and Tomizuka, Masayoshi},journal={IEEE Transactions on Intelligent Transportation Systems (T-ITS)},year={2024},note={presented at 2020 NeurIPS Workshop on Machine Learning for Autonomous Driving}}
Active Exploration in Iterative Gaussian Process Regression for Uncertainty Modeling in Autonomous Racing
Tommaso Benciolini, Chen Tang, Marion Leibold, Catherine Weaver, Masayoshi Tomizuka, and Wei Zhan
IEEE Transactions on Control Systems Technology (T-CST), 2024
@article{benciolini2023active,title={Active Exploration in Iterative Gaussian Process Regression for Uncertainty Modeling in Autonomous Racing},author={Benciolini, Tommaso and Tang, Chen and Leibold, Marion and Weaver, Catherine and Tomizuka, Masayoshi and Zhan, Wei},journal={IEEE Transactions on Control Systems Technology (T-CST)},year={2024},}
Skill-Critic: Refining Learned Skills for Reinforcement Learning
Ce Hao*, Catherine Weaver*, Chen Tang, Kenta Kawamoto, Masayoshi Tomizuka, and Wei Zhan
IEEE Robotics and Automation Letters (RA-L), 2024
accepted for oral presentation at 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
@article{hao2023skill,title={Skill-Critic: Refining Learned Skills for Reinforcement Learning},author={Hao, Ce and Weaver, Catherine and Tang, Chen and Kawamoto, Kenta and Tomizuka, Masayoshi and Zhan, Wei},journal={IEEE Robotics and Automation Letters (RA-L)},year={2024},note={accepted for oral presentation at 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).}}
Guided Online Distillation: Promoting Safe Reinforcement Learning by Offline Demonstration
@inproceedings{li2023gold,title={Guided Online Distillation: Promoting Safe Reinforcement Learning by Offline Demonstration},author={Li, Jinning and Liu, Xinyi and Zhu, Banghua and Jiao, Jiantao and Tomizuka, Masayoshi and Tang, Chen and Zhan, Wei},booktitle={IEEE International Conference on Robotics and Automation (ICRA)},year={2024},}
Pre-training on Synthetic Driving Data for Trajectory Prediction
Yiheng Li*, Seth Z Zhao*, Chenfeng Xu, Chen Tang, Chenran Li, Mingyu Ding, Masayoshi Tomizuka, and Wei Zhan
In EEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024
@inproceedings{li2023pre,title={Pre-training on Synthetic Driving Data for Trajectory Prediction},author={Li, Yiheng and Zhao, Seth Z and Xu, Chenfeng and Tang, Chen and Li, Chenran and Ding, Mingyu and Tomizuka, Masayoshi and Zhan, Wei},booktitle={EEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},year={2024},}
Quantifying Agent Interaction in Multi-agent Reinforcement Learning for Cost-efficient Generalization
Yuxin Chen, Chen Tang, Ran Tian, Chenran Li, Jinning Li, Masayoshi Tomizuka, and Wei Zhan
In Reinforcement Learning Conference (RLC), 2024
Preliminary version was presented as an extended abstract at 2024 International Conference on Autonomous Agents and Multi-agent Systems (AAMAS)
@inproceedings{chen2023quantifying,title={Quantifying Agent Interaction in Multi-agent Reinforcement Learning for Cost-efficient Generalization},author={Chen, Yuxin and Tang, Chen and Tian, Ran and Li, Chenran and Li, Jinning and Tomizuka, Masayoshi and Zhan, Wei},booktitle={Reinforcement Learning Conference (RLC)},year={2024},note={Preliminary version was presented as an extended abstract at 2024 International Conference on Autonomous Agents and Multi-agent Systems (AAMAS)}}
Optimizing Diffusion Models for Joint Trajectory Prediction and Controllable Generation
@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},}
BeTAIL: Behavior Transformer Adversarial Imitation Learning from Human Racing Gameplay
Catherine Weaver, Chen Tang, Ce Hao, Kenta Kawamoto, Masayoshi Tomizuka, and Wei Zhan
@article{weaver2023betail,title={{BeTAIL}: Behavior Transformer Adversarial Imitation Learning from Human Racing Gameplay},author={Weaver, Catherine and Tang, Chen and Hao, Ce and Kawamoto, Kenta and Tomizuka, Masayoshi and Zhan, Wei},journal={IEEE Robotics and Automation Letters (RA-L)},year={2024},}
Learning Online Belief Prediction for Efficient POMDP Planning in Autonomous Driving
@article{huang2023pomdp,title={Learning Online Belief Prediction for Efficient POMDP Planning in Autonomous Driving},author={Huang, Zhiyu and Tang, Chen and Lv, Chen and Tomizuka, Masayoshi and Zhan, Wei},journal={IEEE Robotics and Automation Letters (RA-L)},year={2024},}
Residual-MPPI: Online Policy Customization for Continuous Control
@article{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},journal={arXiv preprint arXiv:2407.00898},year={2024},note={under double-bline review},}
MEReQ: Max-Ent Residual-Q Inverse RL for Sample-Efficient Alignment from Human Intervetion
Yuxin Chen*, Chen Tang*, Chenran Li, Ran Tian, Peter Stone, Masayoshi Tomizuka, and Wei Zhan
@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={arXiv preprint arXiv:2406.16258},year={2024},}
WOMD-Reasoning: A Large-Scale Language Dataset for Interactions and Driving Intentions Reasoning
@article{li2024womd,title={{WOMD-Reasoning}: A Large-Scale Language Dataset for Interactions and Driving Intentions Reasoning},author={Li, Yiheng and Ge, Chongjian and Li, Chenran and Xu, Chenfeng and Tomizuka, Masayoshi and Tang, Chen and Ding, Mingyu and Zhan, Wei},journal={arXiv preprint arXiv:2407.04281},year={2024},}
Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes
Chen Tang*, Ben Abbatematteo*, Jiaheng Hu*, Rohan Chandra, Roberto Martín-Martín, and Peter Stone
Annual Review of Control Robotics and Autonomous Systems, 2024
@article{tang2024survey,title={Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes},author={Tang, Chen and Abbatematteo, Ben and Hu, Jiaheng and Chandra, Rohan and Mart{\'i}n-Mart{\'i}n, Roberto and Stone, Peter},journal={Annual Review of Control Robotics and Autonomous Systems},year={2024},note={To be appeared on ARCRAS in May 2025},}
2023
Double-Iterative Gaussian Process Regression for Modeling Error Compensation in Autonomous Racing
Shaoshu Su, Ce Hao, Catherine Weaver, Chen Tang, Wei Zhan, and Masayoshi Tomizuka
@inproceedings{su2022racing,title={Double-Iterative Gaussian Process Regression for Modeling Error Compensation in Autonomous Racing},author={Su, Shaoshu and Hao, Ce and Weaver, Catherine and Tang, Chen and Zhan, Wei and Tomizuka, Masayoshi},booktitle={22nd IFAC World Congress (IFAC)},year={2023},}
Editing Driver Character: Socially-Controllable Behavior Generation for Interactive Traffic Simulation
presented at 2023 CVPR Workshop on Multi-Agent Behavior: Properties, Computation, and Emergence (MABe) and 2024 IEEE International Conference on Robotics and Automation (ICRA)
@article{chang2023scbg,author={Chang, Wei-Jer and Tang, Chen and Li, Chenran and Hu, Yeping and Tomizuka, Masayoshi and Zhan, Wei},journal={IEEE Robotics and Automation Letters (RA-L)},title={Editing Driver Character: Socially-Controllable Behavior Generation for Interactive Traffic Simulation},year={2023},volume={},number={},pages={1-8},doi={10.1109/LRA.2023.3291897},note={presented at 2023 CVPR Workshop on Multi-Agent Behavior: Properties, Computation, and Emergence (MABe) and 2024 IEEE International Conference on Robotics and Automation (ICRA)}}
Residual Q-Learning: Offline and Online Policy Customization without Value
Chenran Li*, Chen Tang*, Haruki Nishimura, Jean Mercat, Masayoshi Tomizuka, and Wei Zhan
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={featured in Nikkei Robotics}}
2022
Dealing with the unknown: Pessimistic offline reinforcement learning
Jinning Li, Chen Tang, Masayoshi Tomizuka, and Wei Zhan
@inproceedings{jinning2021pessimistic,title={Dealing with the unknown: Pessimistic offline reinforcement learning},author={Li, Jinning and Tang, Chen and Tomizuka, Masayoshi and Zhan, Wei},booktitle={Conference on Robot Learning (CoRL)},pages={1455--1464},year={2022},organization={PMLR},}
@inproceedings{tang2022pseudo,title={Domain Knowledge Driven Pseudo Labels for Interpretable Goal-Conditioned Interactive Trajectory Prediction},author={Sun, Lingfeng and Tang, Chen and Niu, Yaru and Sachdeva, Enna and Choi, Chiho and Misu, Teruhisa and Tomizuka, Masayoshi and Zhan, Wei},booktitle={2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},year={2022},volume={},number={},pages={13034-13041},doi={10.1109/IROS47612.2022.9982147},}
Interventional Behavior Prediction: Avoiding Overly Confident Anticipation in Interactive Prediction
Chen Tang, Wei Zhan, and Masayoshi Tomizuka
In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022
@inproceedings{tang2022ibp,title={Interventional Behavior Prediction: Avoiding Overly Confident
Anticipation in Interactive Prediction},author={Tang, Chen and Zhan, Wei and Tomizuka, Masayoshi},booktitle={2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},year={2022},volume={},number={},pages={11409-11415},doi={10.1109/IROS47612.2022.9981524},}
Hierarchical Planning Through Goal-Conditioned Offline Reinforcement Learning
Jinning Li, Chen Tang, Wei Zhan, and Masayoshi Tomizuka
@article{li2022goalRL,author={Li, Jinning and Tang, Chen and Zhan, Wei and Tomizuka, Masayoshi},journal={IEEE Robotics and Automation Letters (RA-L)},title={Hierarchical Planning Through Goal-Conditioned Offline Reinforcement Learning},year={2022},volume={7},number={4},pages={10216-10223},doi={10.1109/LRA.2022.3190100},}
PreTraM: Self-Supervised Pre-training via Connecting Trajectory and Map
Chenfeng Xu*, Tian Li*, Chen Tang, Lingfeng Sun, Kurt Keutzer, Masayoshi Tomizuka, Alireza Fathi, and Wei Zhan
In European Conference on Computer Vision (ECCV), 2022
@inproceedings{xu2022PreTraM,title={PreTraM: Self-Supervised Pre-training via Connecting Trajectory and Map},author={Xu, Chenfeng and Li, Tian and Tang, Chen and Sun, Lingfeng and Keutzer, Kurt and Tomizuka, Masayoshi and Fathi, Alireza and Zhan, Wei},booktitle={European Conference on Computer Vision (ECCV)},year={2022},pages={34--50},}
Outracing Human Racers with Model-based Planning and Control for Time-trial Racing
Ce Hao, Chen Tang, Eric Bergkvist, Catherine Weaver, Liting Sun, Wei Zhan, and Masayoshi Tomizuka
@article{hao2022outracing,title={Outracing Human Racers with Model-based Planning and Control for Time-trial Racing},author={Hao, Ce and Tang, Chen and Bergkvist, Eric and Weaver, Catherine and Sun, Liting and Zhan, Wei and Tomizuka, Masayoshi},journal={arXiv preprint arXiv:2211.09378},year={2022},}
Dissertation
Designing Explainable Autonomous Driving System for Trustworthy Interaction
@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},}
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},}
2019
Sensors 2019
A Remote Control Strategy for an Autonomous Vehicle with Slow Sensor Using Kalman Filtering and Dual-Rate Control
Ángel Cuenca, Wei Zhan, Julián Salt, José Alcaina, Chen Tang, and Masayoshi Tomizuka
@article{cuenca2019remote,title={A Remote Control Strategy for an Autonomous Vehicle with Slow Sensor Using Kalman Filtering and Dual-Rate Control},author={Cuenca, {\'A}ngel and Zhan, Wei and Salt, Juli{\'a}n and Alcaina, Jos{\'e} and Tang, Chen and Tomizuka, Masayoshi},journal={Sensors},volume={19},number={13},pages={2983},year={2019},publisher={Multidisciplinary Digital Publishing Institute},}
IEEE IV 2019
Toward Modularization of Neural Network Autonomous Driving Policy Using Parallel Attribute Networks
@inproceedings{xu2019toward,title={Toward Modularization of Neural Network Autonomous Driving Policy Using Parallel Attribute Networks},author={Xu, Zhuo and Chang, Haonan and Tang, Chen and Liu, Changliu and Tomizuka, Masayoshi},booktitle={2019 IEEE Intelligent Vehicles Symposium (IV)},pages={1400--1407},year={2019},organization={IEEE},}
Disturbance-observer-based tracking controller for neural network driving policy transfer
Chen Tang*, Zhuo Xu*, and Masayoshi Tomizuka
IEEE Transactions on Intelligent Transportation Systems (T-ITS), 2019
@article{tang2019disturbance,title={Disturbance-observer-based tracking controller for neural network driving policy transfer},author={Tang, Chen and Xu, Zhuo and Tomizuka, Masayoshi},journal={IEEE Transactions on Intelligent Transportation Systems (T-ITS)},volume={21},number={9},pages={3961--3972},year={2019},publisher={IEEE},}
@inproceedings{tang2019adaptive,title={Adaptive Probabilistic Vehicle Trajectory Prediction Through Physically Feasible Bayesian Recurrent Neural Network},author={Tang, Chen and Chen, Jianyu and Tomizuka, Masayoshi},booktitle={2019 International Conference on Robotics and Automation (ICRA)},pages={3846--3852},year={2019},organization={IEEE},}
2018
Zero-shot Deep Reinforcement Learning Driving Policy Transfer for Autonomous Vehicles based on Robust Control
Zhuo Xu*, Chen Tang*, and Masayoshi Tomizuka
In 2018 21st International Conference on Intelligent Transportation Systems (ITSC), 2018
@inproceedings{xu2018zero,title={Zero-shot Deep Reinforcement Learning Driving Policy Transfer for Autonomous Vehicles based on Robust Control},author={Xu, Zhuo and Tang, Chen and Tomizuka, Masayoshi},booktitle={2018 21st International Conference on Intelligent Transportation Systems (ITSC)},pages={2865--2871},year={2018},organization={IEEE},note={Best Student-Paper Runner-up},}
Continuous Decision Making for On-Road Autonomous Driving under Uncertain and Interactive Environments
Jianyu Chen, Chen Tang, Long Xin, Shengbo Eben Li, and Masayoshi Tomizuka
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},}
2017
A Highly Sensitive Graphene Woven Fabric Strain Sensor for Wearable Wireless Musical Instruments
Xu Liu, Chen Tang, Xiaohan Du, Shuai Xiong, Siyuan Xi, Yuefeng Liu, Xi Shen, Qingbin Zheng, Zhenyu Wang, Ying Wu, and others
@article{liu2017highly,title={A Highly Sensitive Graphene Woven Fabric Strain Sensor for Wearable Wireless Musical Instruments},author={Liu, Xu and Tang, Chen and Du, Xiaohan and Xiong, Shuai and Xi, Siyuan and Liu, Yuefeng and Shen, Xi and Zheng, Qingbin and Wang, Zhenyu and Wu, Ying and others},journal={Materials Horizons},volume={4},number={3},pages={477--486},year={2017},publisher={Royal Society of Chemistry},}
2015
IEEE MEMS 2015
The Capillary Number Effect on the Capture Efficiency of Cancer Cells on Composite Microfluidic Filtration Chips
Cong Zhao, Rui Xu, Kui Song, Dayu Liu, Shuo Ma, Chen Tang, Chun Liang, Yitshak Zohar, and Yi-Kuen Lee
In 2015 28th IEEE International Conference on Micro Electro Mechanical Systems (MEMS), 2015
@inproceedings{zhao2015capillary,title={The Capillary Number Effect on the Capture Efficiency of Cancer Cells on Composite Microfluidic Filtration Chips},author={Zhao, Cong and Xu, Rui and Song, Kui and Liu, Dayu and Ma, Shuo and Tang, Chen and Liang, Chun and Zohar, Yitshak and Lee, Yi-Kuen},booktitle={2015 28th IEEE International Conference on Micro Electro Mechanical Systems (MEMS)},pages={459--462},year={2015},organization={IEEE},}
Quantifying Agent Interaction in Multi-agent Reinforcement Learning for Cost-efficient GeneralizationIn Reinforcement Learning Conference (RLC), 2024Preliminary version was presented as an extended abstract at 2024 International Conference on Autonomous Agents and Multi-agent Systems (AAMAS)
Editing Driver Character: Socially-Controllable Behavior Generation for Interactive Traffic SimulationIEEE Robotics and Automation Letters (RA-L), 2023presented at 2023 CVPR Workshop on Multi-Agent Behavior: Properties, Computation, and Emergence (MABe) and 2024 IEEE International Conference on Robotics and Automation (ICRA)
