About me

I am a first-year master student in the Robotics Institute at Carnegie Mellon University, working with Prof.Fernando De La Torre and Dr.Joseph K J on evaluation for vision language models.

During my undergrad, I worked with Prof.Huazhe Xu at Tsinghua University on reinforcement learning. I have also collaborated with Dr.Ruohan Zhang at Prof.Jiajun Wu’s Lab at Stanford University on Human-robot interaction. I also spent a summer doing internship at Microsoft WebXT group, Bing Ads team.


  • One paper accepted at Neurips’23
  • I joined Carnegie Mellon Univerisy for the Master’s in Robotics (MSR) program!
  • I graduated from the Computer Science Department of Tsinghua University with a Bachelor’s degree in engineering!
  • Our paper “A Dual Representation Framework for Robot Learning with Human Guidance” is accepted by CORL’22
  • I gave a talk at Stanford Vision & Learning Lab
  • I became a visiting research intern at Stanford in July, 2022
  • I became a Deep Learning Intern at Microsoft China in July, 2021
  • Our paper “The Origin of CBRAM With High Linearity, ON/OFF Ratio, and State Number for Neuromorphic Computing” is accpeted at IEEE-TED, 2021


  • Robot Learning with Text-to-Image Generation Models
    • Explore the possibility of utilizing the knowledge embedded in pre-trained text2image diffusion models to provide visual guidance for robot learning, without any expert demonstrations or human-designed reward functions.
    • Propose a pipeline using image editing techniques to generate visual goals for example-based visual RL, and experiments in simulation and real-world validates the effectiveness of the proposed method.
  • Dual Representation for Robot Learning under Human Guidance
    • Focus on designing sample-efficient preference learning algorithms that can achieve high performance in both oracle and human experiments.
    • Incorporate scene graph as a high-level abstraction representation to facilitate better query selection algorithms in preference learning, which lead to SOTA performance in reward estimation.
  • Concurrent Multi-task Learning
    • Come up with a new task setting, “concurrent multi-task learning”, where the demonstrator demonstrates multiple tasks in a single demonstration, and the algorithm tries to learn all separate tasks with these “concurrent” demonstrations.
    • Propose a EM-based Neural Network learning algorithm that can effectively learns separate tasks without requiring extra labels.
  • Neuromorphic Computing
    • Explore the origin of CBRAM with High Linearity, ON/OFF Ratio, and State Number, which are crucial for a successful neuromorphic computation network(such as deep-learning neural networks) built from these CBRAMs as neurons.
    • Build a Powerful Neural Network using this CBRAM model as neurons which can solve the MNIST classification problem with SOTA performance(92%) compared to previous neuromorphic computation networks(80%).