Yaru Niu

yaru.jpg

yarun@andrew.cmu.edu

5000 Forbes Avenue

Pittsburgh, PA 15213

Hi! I am a first-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) can be interpreted as a scholar in elegant taste by ancient Chinese.

CV /  Google Scholar /  Twitter /  Github /  Linkedin

News

Jan, 2023 Our work on group distributionally robust reinforcement learning is accepted to AISTATS 2023.
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!

Selected Publications

Notation * indicates equal contributions.

  1. GOATS: Goal Sampling Adaptation for Scooping with Curriculum Reinforcement Learning
    Yaru Niu, Shiyu Jin*, Zeqing Zhang*, Jiacheng Zhu, Ding Zhao, and Liangjun Zhang
    In submission, 2023
    Abridged in ICRA 2023 Workshop on Representing and Manipulating Deformable Objects [PDF]
    PDF Webpage Talk Video
  2. Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables
    Mengdi Xu, Peide Huang,  Yaru Niu, Visak Kumar, Jielin Qiu, Chao Fang, Kuan-Hui Lee, Xuewei Qi, Henry Lam, Bo Li, and Ding Zhao
    In International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
    PDF Webpage
  3. Domain Knowledge Driven Pseudo Labels for Interpretable Goal-Conditioned Interactive Trajectory Prediction
    Lingfeng Sun*, Chen Tang*,  Yaru Niu, Enna Sachdeva, Chiho Choi, Teruhisa Misu, Masayoshi Tomizuka, and Wei Zhan
    In International Conference on Intelligent Robots and Systems (IROS), 2022
    PDF Slides Talk
  4. Adaptable and Scalable Multi-Agent Graph-Attention Communication
    Yaru Niu
    Committee:  Matthew Gombolay,  Ayanna Howard,  and Sonia Chernova
    Master’s Thesis, Georgia Institute of Technology, 2022
    HTML PDF
  5. Learning Interpretable, High-Performing Policies for Autonomous Driving
    Rohan Paleja*,  Yaru Niu*, Andrew Silva, Chace Ritchie, Sugju Choi, and Matthew Gombolay
    In Robotics: Science and Systems (RSS), 2022
    HTML PDF Code Talk
  6. Multi-Agent Graph-Attention Communication and Teaming
    Yaru Niu*, Rohan Paleja*, and Matthew Gombolay
    In International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2021 (Oral)
    Best Paper Award at ICCV 2021 Mair2 Workshop [PDF] [Spotlight Talk]
    PDF Code Talk
  7. Real-Time Whole-Body Imitation by Humanoid Robots and Task-Oriented Teleoperation using an Analytical Mapping Method and Quantitative Evaluation
    Zhijun Zhang*,  Yaru Niu*, Ziyi Yan, and Shuyang Lin
    Applied Sciences, 2018
    HTML PDF Code Video