Jiachen Li, Ph.D.

Assistant Professor at University of California, Riverside (UCR)
I'm

About Me

Assistant Professor

I am a tenure-track assistant professor in the Department of Electrical and Computer Engineering (ECE) and the Department of Computer Science and Engineering (CSE) at the University of California, Riverside (UCR). I am the Director of Trustworthy Autonomous Systems Laboratory (TASL) and a core faculty of Center for Robotics and Intelligent Systems (CRIS). Before joining UCR, I was a Postdoctoral Scholar at Stanford University working with Prof. Mykel J. Kochenderfer at Stanford Intelligent Systems Laboratory (SISL), Stanford Center for AI Safety, and Stanford Artificial Intelligence Laboratory (SAIL). I obtained my Ph.D. degree from the University of California, Berkeley working with Prof. Masayoshi Tomizuka at Mechanical Systems Control Laboratory, Berkeley AI Research (BAIR), and Berkeley DeepDrive (BDD).

Attention! I am actively looking for multiple highly motivated Ph.D. students (fully funded), master students, undergraduate students, and research interns to join my lab in Fall 2025. If you are interested, please follow the application instructions HERE. Please feel free to send me an email if any questions.

  • Google Scholar: HERE
  • E-mail: jiachen.li AT ucr.edu
  • UC Riverside Profile: HERE

 

Research Interest

My research interest lies in the broad intersection of robotics, trustworthy AI & ML, reinforcement learning, control and optimization and their applications to intelligent autonomous systems (e.g., autonomous vehicles, mobile robots, drones, cyber-physical systems). I am particularly interested in human-robot interactions and multi-agent systems. Please refer to the Research section or my lab website for more details about my recent research!

I am open to research discussion and collaboration, please feel free to get in touch!

News

Please check this webpage for the latest news about myself and my lab!


Curriculum Vitae


Click here to download my full CV (08/2024).

Research

The ultimate goal of my research is to build trustworthy, interactive, and human-centered autonomous embodied agents that can perceive, understand, and reason about the physical world; safely interact and collaborate with humans; and efficiently coordinate with other intelligent agents so that they can benefit society in daily lives. To achieve this goal, I have been pursuing interdisciplinary research and unifying the techniques and tools from robotics, trustworthy AI/ML, deep reinforcement learning, control theory, optimization, and computer vision.


Please check this webpage for my latest featured research topics!


Publications

The list below may not be up to date, please check this webpage or Google Scholar for my latest publications!

 

Under Review / Preprints

SoNIC: Safe Social Navigation with Adaptive Conformal Inference and Constrained Reinforcement Learning
submitted to IEEE Robotics and Automation Letters (RA-L), under review
J. Yao, X. Zhang, Y. Xia, Z. Wang, A. K. Roy-Chowdhury, and J. Li
arXiv
Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation
submitted to IEEE Transactions on Robotics (T-RO), under review
J. Li*, C. Hua*, H. Ma, J. Park, V. Dax, and M. J. Kochenderfer
arXiv
CMP: Cooperative Motion Prediction with Multi-Agent Communication
submitted to IEEE Robotics and Automation Letters (RA-L), under review
Z. Wu*, Y. Wang*, Z. Wang*, H. Ma, Z. Li, H. Qiu, and J. Li
arXiv
Adaptive Prediction Ensemble: Improving Out-of-Distribution Generalization of Motion Forecasting
submitted to IEEE Robotics and Automation Letters (RA-L), under review
J. Li, J. Li, S. Bae, and D. Isele
arXiv
Self-supervised Multi-future Occupancy Forecasting for Autonomous Driving
submitted to IEEE Robotics and Automation Letters (RA-L), under review
B. Lange, M. Itkina, J. Li, and M. J. Kochenderfer
arXiv
Importance Sampling-Guided Meta-Training for Intelligent Agents in Highly Interactive Environments
submitted to IEEE Robotics and Automation Letters (RA-L), under review
M. Arief*, M. Timmerman*, J. Li, D. Isele, and M. J. Kochenderfer
arXiv
Integrating Graph and Recurrent Neural Networks for Spatiotemporal Reasoning
submitted to IEEE Transactions on Neural Networks and Learning Systems (T-NNLS), under review
V. Dax, Z. Li, X. Zhang, H. Shekhar, J. Li, and M. J. Kochenderfer
arXiv (coming soon)
The Generalization Gap of Locally Unordered GNNs
under review
V. Dax, J. Li, and M. J. Kochenderfer
arXiv (coming soon)

 

2024

Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation
IEEE Transactions on Robotics (T-RO), 2024
J. Li, D. Isele, K. Lee, J. Park, K. Fujimura, and M. J. Kochenderfer
arXiv IEEE
MATRIX: Multi-Agent Trajectory Generation with Diverse Contexts
International Conference on Robotics and Automation (ICRA 2024)
Z. Xu*, R. Zhou*, Y. Yin*, H. Gao, M. Tomizuka, and J. Li
arXiv
Scene Informer: Anchor-based Occlusion Inference and Trajectory Prediction in Partially Observable Environments
International Conference on Robotics and Automation (ICRA 2024)
B. Lange, J. Li, and M. J. Kochenderfer
arXiv
Rank2Tell: A Multimodal Dataset for Joint Driving Importance Ranking and Reasoning
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024
E. Sachdeva*, N. Agarwal*, S. Chundi, S. Roelofs, J. Li, C. Choi, M. J. Kochenderfer, and B. Dariush
arXiv IEEE
Predicting Future Spatiotemporal Occupancy Grids with Semantics for Autonomous Driving
IEEE Intelligent Vehicles Symposium (IV 2024)
M. Toyungyernsub, E. Yel, J. Li, and M. J. Kochenderfer
arXiv IEEE
ELA: Exploited Level Augmentation for Offline Learning in Zero-Sum Games
International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2024)
S. Lei*, K. Lee*, L. Li, J. Park, and J. Li
AAMAS arXiv

 

2023

Disentangled Neural Relational Inference for Interpretable Motion Prediction
IEEE Robotics and Automation (RA-L), 2023
V. M. Dax, J. Li, E. Sachdeva, N. Agarwal, and M. J. Kochenderfer
arXiv IEEE
Pedestrian Crossing Action Recognition and Trajectory Prediction with 3D Human Keypoints
2023 International Conference on Robotics and Automation (ICRA)
J. Li, X. Shi*, F. Chen*, J. Stroud*, Z. Zhang, T. Lan, J. Mao, J. Kang, K. Refaat, W. Yang, E. Le and C. Li
arXiv
Robust Driving Policy Learning with Guided Meta Reinforcement Learning
IEEE International Conference on Intelligent Transportation Systems (ITSC), 2023
K. Lee*, J. Li*, D. Isele, J. Park, K. Fujimura, and M. J. Kochenderfer
arXiv
Game Theory-Based Simultaneous Prediction and Planning for Autonomous Vehicle Navigation in Crowded Environments
IEEE International Conference on Intelligent Transportation Systems (ITSC), 2023
K. Li, Y. Chen, M. Shan, J. Li, S. Worrall, and E. Nebot
arXiv
DRAMA: Joint Risk Localization and Reasoning in Driving Scenarios
In IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023
S. Malla, C. Choi, J. H. Choi, I. Dwivedi, and J. Li
WACV arXiv
A Cognition-Inspired Trajectory Prediction Method for Vehicles in Interactive Scenarios
IET Intelligent Transport Systems
S. Xie, J. Li and J. Wang
IET

 

2022

Interaction Modeling with Multiplex Attention
In Neural Information Processing Systems (NeurIPS), 2022.
F. Sun, I. Kauvar, R. Zhang, J. Li, M. J. Kochenderfer, J. Wu, and N. Haber
NeurIPS Website arXiv
Learning Physical Dynamics with Subequivariant Graph Neural Networks
In Neural Information Processing Systems (NeurIPS), 2022.
J. Han, W. Huang, H. Ma, J. Li, J. B. Tenenbaum, and C. Gan
NeurIPS arXiv
Important Object Identification with Semi-Supervised Learning for Autonomous Driving
2022 International Conference on Robotics and Automation (ICRA)
J. Li*, H. Gang*, H. Ma, M. Tomizuka, and C. Choi
IEEE arXiv
Grouptron: Dynamic Multi-Scale Graph Convolutional Networks for Group-Aware Dense Crowd Trajectory Forecasting
2022 International Conference on Robotics and Automation (ICRA)
R. Zhou, H. Zhou, M. Tomizuka, J. Li*, and Z. Xu*
IEEE arXiv
Dynamics-Aware Spatiotemporal Occupancy Prediction in Urban Environments
In IEEE/RSJ International Conference on Robotics and Systems (IROS), 2022.
M. Toyungyernsub, E. Yel, J. Li, and M. J. Kochenderfer
IEEE arXiv
Multi-Objective Diverse Human Motion Prediction with Knowledge Distillation
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR Oral)
H. Ma, J. Li, R. Hosseini, M. Tomizuka and C. Choi
CVPR Video
Graph Q-Learning for Combinatorial Optimization
Deep Reinforcement Learning Workshop, NeurIPS 2022
V. Dax, J. Li, K. Leahy, and M. J. Kochenderfer
OpenReview

 

2021

RAIN: Reinforced Hybrid Attention Inference Network for Motion Forecasting
2021 IEEE/CVF International Conference on Computer Vision (ICCV)
J. Li, F. Yang, H. Ma, S. Malla, M. Tomizuka, and C. Choi
ICCV arXiv
LOKI: Long Term and Key Intentions for Trajectory Prediction
2021 IEEE/CVF International Conference on Computer Vision (ICCV)
H. Gang*, H. Girase*, S. Malla, J. Li, A. Kanehara, K. Mangalam, and C. Choi
ICCV arXiv
Shared Cross-Modal Trajectory Prediction for Autonomous Driving
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR Oral)
C. Choi, J. H. Choi, J. Li, and S. Malla
CVPR arXiv
Spatio-Temporal Graph Dual-Attention Network for Multi-Agent Prediction and Tracking
IEEE Transactions on Intelligent Transportation Systems
J. Li, H. Ma, Z. Zhang, J. Li, and M. Tomizuka
IEEE arXiv
Continual Multi-agent Interaction Behavior Prediction with Conditional Generative Memory
IEEE Robotics and Automation Letters (RA-L)
H. Ma*, Y. Sun*, J. Li, M. Tomizuka, and C. Choi
IEEE PDF
Reinforcement Learning for Autonomous Driving with Latent State Inference and Spatial-Temporal Relationships
2021 International Conference on Robotics and Automation (ICRA)
X. Ma, J. Li, MJ. Kochenderfer, D. Isele, and K. Fujimura
IEEE arXiv
Spectral Temporal Graph Neural Network for Trajectory Prediction
2021 International Conference on Robotics and Automation (ICRA)
D. Cao*, J. Li*, H. Ma, and M. Tomizuka
IEEE arXiv
Multi-agent Driving Behavior Prediction across Different Scenarios with Self-supervised Domain Knowledge
2021 IEEE Intelligent Transportation Systems Conference (ITSC)
H. Ma*, Y. Sun*, J. Li, and M. Tomizuka
IEEE PDF
Autonomous Driving Strategies at Intersections: Scenarios, State-of-the-Art, and Future Outlooks
2021 IEEE Intelligent Transportation Systems Conference (ITSC)
L. Wei, Z. Li, J. Gong, C. Gong, and J. Li
IEEE arXiv
Orientation-Aware Planning for Parallel Task Execution of Omni-Directional Mobile Robot
2021 IEEE/RSJ International Conference on Robotics and Systems (IROS)
C. Gong, X. Zhou, Z. Li, J. Li, J. Gong, and J. Zhou
IEEE arXiv

 

2020

EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning
In Proceedings of the Neural Information Processing Systems (NeurIPS) 2020.
J. Li*, F. Yang*, M. Tomizuka, and C. Choi
NeurIPS Website arXiv
Generic Tracking and Probabilistic Prediction Framework and Its Application in Autonomous Driving
IEEE Transactions on Intelligent Transportation Systems
J. Li, W. Zhan, Y. Hu, and M. Tomizuka
IEEE arXiv
Social-WaGDAT: Interaction-Aware Trajectory Prediction via Wasserstein Graph Double-Attention Network
arXiv preprint arXiv: 2002.06241
J. Li, H. Ma, Z. Zhang, and M. Tomizuka
arXiv

 

2019

Interaction-aware Multi-agent Tracking and Probabilistic Behavior Prediction via Adversarial Learning
2019 IEEE International Conference on Robotics and Automation (ICRA)
J. Li*, H. Ma*, and M. Tomizuka
IEEE arXiv
Conditional Generative Neural System for Probabilistic Trajectory Prediction
2019 IEEE/RSJ International Conference on Robotics and Systems (IROS).
J. Li, H. Ma, and M. Tomizuka
IEEE arXiv
Coordination and Trajectory Prediction for Vehicle Interactions via Bayesian Generative Modeling
2019 IEEE Intelligent Vehicles Symposium (IV)
J. Li, H. Ma, W. Zhan, and M. Tomizuka
IEEE arXiv
Wasserstein Generative Learning with Kinematic Constraints for Probabilistic Interactive Driving Behavior Prediction
2019 IEEE Intelligent Vehicles Symposium (IV)
H. Ma, J. Li, W. Zhan, and M. Tomizuka
IEEE

 

2018

Generic Probabilistic Interactive Situation Recognition and Prediction: From Virtual to Real
2018 IEEE Intelligent Transportation Systems Conference (ITSC)
J. Li, H. Ma, W. Zhan, and M. Tomizuka
IEEE arXiv
Towards a Fatality-Aware Benchmark of Probabilistic Reaction Prediction in Highly Interactive Driving Scenarios
2018 IEEE Intelligent Transportation Systems Conference (ITSC)
W. Zhan, L. Sun, Y. Hu, J. Li, and M. Tomizuka
IEEE arXiv
Generic Vehicle Tracking Framework Capable of Handling Occlusions Based on Modified Mixture Particle Filter
2018 IEEE Intelligent Vehicles Symposium (IV) (Oral)
J. Li, W. Zhan, and M. Tomizuka
IEEE arXiv

 

2017

Safe and Feasible Motion Generation for Autonomous Driving via Constrained Policy Net
2017 Annual Conference of the Industrial Electronics Society (IECON)
W. Zhan, J. Li, Y. Hu, and M. Tomizuka
IEEE

 

2016

A Novel Variable Selection Approach for Redundant Information Elimination Purpose of Process Control
IEEE Transactions on Industrial Electronics
J. Li, C. Duan, and Z. Fei
IEEE
Finite-time H∞ Control of Switched Systems with Mode-dependent Average Dwell Time
Journal of the Franklin Institute
S. Shi, Z. Fei, and J. Li
ELSEVIER
A Variable Selection Aided Residual Generator Design Approach for Process Control and Monitoring
Neurocomputing
C. Duan, Z. Fei, and J. Li
ELSEVIER

 

* indicates equal contribution/advising

Academic Services

Please check my CV for a complete list of my academic services!

Join My Lab

I am currently seeking multiple highly motivated talents to join my laboratory as Ph.D. students (fully funded), master students, undergraduate students, or onsite/remote research interns (outside of UC Riverside), visiting scholars, or postdoctoral scholars. If you are interested in working with me, please check my lab website for application instructions.

Prospective students must also submit an application for a certain program on the UCR official website before the corresponding deadline. If you are applying for a Ph.D. program in EE or CS, please indicate Prof. Jiachen Li as your prospective advisor in the UCR official application form and Statement of Purpose.

© Jiachen Li (08/2024)