5000 Forbes Avenue
Pittsburgh, PA 15213
Hi! I am a PhD student at CMU Safe AI Lab, advised by Prof. Ding Zhao. Previously, I received an M.S. in Electrical and Computer Engineering at Georgia Tech, working with Prof. Matthew Gombolay. Before that, I received a B.E. in Intelligence Science and Technology at South China University of Technology. I also spent wonderful time as a research intern at Baidu Research with Dr. Liangjun Zhang, and at Berkeley with Prof. Masayoshi Tomizuka.
My research goal is to build methodologies for collaborative, interpretable, and reliable intelligent robotic systems that can interact with complex environments (including humans) around them. Thus, my research interest lies at the intersection of robotics and machine learning, and their applications in multi-agent coordination, autonomous driving, and human-robot interaction.
My Chinese name is 牛雅儒 (Niu-Ya-Ru), where my first name (雅儒) can be interpreted as a scholar in elegant taste by ancient Chinese.
|Nov, 2022||We are organizing the RoboDepth (Robust Out-of-distribution Depth prediction) Challenge at ICRA 2023.|
|Jun, 2022||Our work on interactive motion prediction for autonomous driving is accepted to IROS 2022.|
|May, 2022||I graduate from Georgia Tech. Thank you, GT. Check out my MS Thesis.|
|Apr, 2022||I will join Safe AI Lab at CMU as a PhD student starting from Fall 2022.|
|Apr, 2022||Our work Interpretable Continuous Control Tree (ICCT) is accepted to RSS 2022!|
|Jan, 2022||I start my research internship at Baidu Research in Sunnyvale.|
|Oct, 2021||Our work Multi-Agent Graph-Attention Communication (MAGIC) receives the Best Paper Award at ICCV 2021 Mair2 Workshop!|
|Jan, 2021||I will serve as a TA for CS 4641 Machine Learning (Spring 2021).|
|Dec, 2020||Our paper on multi-agent reinforcement learning (MAGIC) is accepted to AAMAS 2021 for oral presentation!|
|Aug, 2020||I will serve as a TA for CS 4731/7632 Game AI (Fall 2020). Check previous offering.|
Notation * indicates co-first authors.
Group Distributionally Robust Reinforcement Learning with Hierarchical Latent VariablesIn Preprint, under review, 2022
Domain Knowledge Driven Pseudo Labels for Interpretable Goal-Conditioned Interactive Trajectory PredictionIn International Conference on Intelligent Robots and Systems (IROS), 2022
Adaptable and Scalable Multi-Agent Graph-Attention CommunicationMaster’s Thesis, Georgia Institute of Technology, 2022
Learning Interpretable, High-Performing Policies for Autonomous DrivingIn Robotics: Science and Systems (RSS), 2022
Real-Time Whole-Body Imitation by Humanoid Robots and Task-Oriented Teleoperation using an Analytical Mapping Method and Quantitative EvaluationApplied Sciences, 2018