5000 Forbes Avenue
Pittsburgh, PA 15213
Hi! I am a second-year 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) means a scholar with elegance in ancient Chinese.
|Jul, 2023||We release the report of RoboDepth (Robust Out-of-distribution Depth prediction) Challenge at ICRA 2023. The event recording is available here.|
|Jun, 2023||Our work on goal-conditioned curriculum reinforcement leanring for robotic scooping is accepted to IROS 2023.|
|Jan, 2023||Our work on group distributionally robust reinforcement learning is accepted to AISTATS 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!|
|Dec, 2020||Our paper on multi-agent reinforcement learning (MAGIC) is accepted to AAMAS 2021 for oral presentation!|
Notation * indicates equal contributions.
GOATS: Goal Sampling Adaptation for Scooping with Curriculum Reinforcement LearningIn International Conference on Intelligent Robots and Systems (IROS), 2023
Abridged in ICRA 2023 Workshop on Representing and Manipulating Deformable Objects [PDF] [Spotlight Talk]
Learning Interpretable, High-Performing Policies for Continuous ControlIn submission, 2023
Group Distributionally Robust Reinforcement Learning with Hierarchical Latent VariablesIn International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Also appeared in 5th Symposium on Advances in Approximate Bayesian Inference
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