World's Best Scientists 2026 revealed!
Award Badge
Rising Stars
2025

D-Index & Metrics

Rising Stars

D-Index
44
Citations
18197
World Ranking
476
National Ranking
74

Computer Science

D-Index
44
Citations
17315
World Ranking
7375
National Ranking
3215

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Yuke Zhu is affiliated with The University of Texas at Austin in the United States. Their research spans multiple areas within computer science and engineering, with a focus on robotics and machine learning applications. They have contributed extensively to topics like robot manipulation and learning, reinforcement learning in robotics, and multimodal machine learning applications.

Their recent publications include:

  • Multi-Stage Learning for Grasp-Constrained Object Manipulation with a Simulated Panda Robot, 2025, arXiv (Cornell University)
  • Foundation models in robotics: Applications, challenges, and the future, 2024, The International Journal of Robotics Research
  • Voyager: An Open-Ended Embodied Agent with Large Language Models, 2023, arXiv (Cornell University)
  • Coopernaut: End-to-End Driving with Cooperative Perception for Networked Vehicles, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Yuke Zhu regularly publishes in the following venues:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • The International Journal of Robotics Research
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2022 International Conference on Robotics and Automation (ICRA)

Their primary fields of study include computer science and engineering, with significant work in subfields such as:

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Control and Systems Engineering
  • Biomedical Engineering
  • Mechanical Engineering

The main research topics covered by Yuke Zhu are:

  • Robot Manipulation and Learning
  • Reinforcement Learning in Robotics
  • Multimodal Machine Learning Applications
  • Human Pose and Action Recognition
  • Domain Adaptation and Few-Shot Learning
  • Robotic Path Planning Algorithms
  • Topic Modeling

Throughout their career, Yuke Zhu has frequently collaborated with several coauthors, including:

  • Anima Anandkumar
  • Linxi Fan
  • Ajay Mandlekar
  • Guanzhi Wang
  • Yunfan Jiang

Best Publications

  • Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations

    Ranjay Krishna;Yuke Zhu;Oliver Groth;Justin Johnson

  • Target-driven visual navigation in indoor scenes using deep reinforcement learning

    Yuke Zhu;Roozbeh Mottaghi;Eric Kolve;Joseph J. Lim

  • Scene Graph Generation by Iterative Message Passing

    Danfei Xu;Yuke Zhu;Christopher B. Choy;Li Fei-Fei

  • DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion

    Chen Wang;Danfei Xu;Yuke Zhu;Roberto Martin-Martin

  • Visual7W: Grounded Question Answering in Images

    Yuke Zhu;Oliver Groth;Michael Bernstein;Li Fei-Fei

  • Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations

    Ranjay Krishna;Yuke Zhu;Oliver Groth;Justin Johnson

  • AI2-THOR: An Interactive 3D Environment for Visual AI

    Eric Kolve;Roozbeh Mottaghi;Daniel Gordon;Yuke Zhu

  • Voyager: An Open-Ended Embodied Agent with Large Language Models

    Unknown

  • Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks

    Michelle A. Lee;Yuke Zhu;Krishnan Srinivasan;Parth Shah

  • Reinforcement and Imitation Learning for Diverse Visuomotor Skills

    Yuke Zhu;Ziyu Wang;Josh Merel;Andrei A. Rusu

  • Reasoning about Object Affordances in a Knowledge Base Representation

    Yuke Zhu;Alireza Fathi;Li Fei-Fei

  • MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge

    Unknown

  • VIMA: General Robot Manipulation with Multimodal Prompts

    Unknown

  • robosuite: A Modular Simulation Framework and Benchmark for Robot Learning.

    Yuke Zhu;Josiah Wong;Ajay Mandlekar;Roberto Martín-Martín

  • Learning task-oriented grasping for tool manipulation from simulated self-supervision:

    Kuan Fang;Yuke Zhu;Animesh Garg;Animesh Garg;Andrey Kurenkov

  • Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks

    Michelle A. Lee;Yuke Zhu;Peter Zachares;Matthew Tan

  • Neural Task Programming: Learning to Generalize Across Hierarchical Tasks

    Danfei Xu;Suraj Nair;Yuke Zhu;Julian Gao

  • Visual Semantic Planning Using Deep Successor Representations

    Yuke Zhu;Daniel Gordon;Eric Kolve;Dieter Fox

  • 6-PACK: Category-level 6D Pose Tracker with Anchor-Based Keypoints

    Chen Wang;Roberto Martin-Martin;Danfei Xu;Jun Lv

  • Coopernaut: End-to-End Driving with Cooperative Perception for Networked Vehicles

    Unknown

  • Neural Task Graphs: Generalizing to Unseen Tasks From a Single Video Demonstration

    De-An Huang;Suraj Nair;Danfei Xu;Yuke Zhu

  • Adversarially Robust Policy Learning: Active construction of physically-plausible perturbations

    Ajay Mandlekar;Yuke Zhu;Animesh Garg;Li Fei-Fei

  • SURREAL: Open-Source Reinforcement Learning Framework and Robot Manipulation Benchmark

    Linxi Fan;Yuke Zhu;Jiren Zhu;Zihua Liu

  • Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations

    Zhenyu Jiang;Yifeng Zhu;Maxwell Svetlik;Kuan Fang

Frequent Co-Authors

Li Fei-Fei
Li Fei-Fei Stanford University
Silvio Savarese
Silvio Savarese Stanford University
Animesh Garg
Animesh Garg University of Toronto
Anima Anandkumar
Anima Anandkumar Nvidia (United Kingdom)
Jeannette Bohg
Jeannette Bohg Stanford University
Abhinav Gupta
Abhinav Gupta Carnegie Mellon University
Ali Farhadi
Ali Farhadi University of Washington
Roozbeh Mottaghi
Roozbeh Mottaghi University of Washington
Michael S. Bernstein
Michael S. Bernstein Stanford University
Peter Stone
Peter Stone The University of Texas at Austin

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring online education can help you shape a flexible career in computer science or related fields. Many students search for the most affordable online masters options to balance quality with cost, a crucial factor when investing in advanced studies.

Leadership is another desirable skillset in tech. If you’re considering a management or academic career, pursuing an online doctorate leadership degree or the cheapest online doctorate in educational leadership can expand your credentials. These programs are designed for working professionals and can often be completed remotely, maximizing convenience.

For those seeking a quicker entry into the tech workforce, an associate’s degree may be ideal. Understanding what is the easiest associate's degree to get can help you select a program that matches your learning style and career goals.

No matter your path, online degrees make it easier to start or advance your computer science career—offering flexibility, affordability, and a variety of choices.

Best Scientists Citing Yuke Zhu

Trending Scientists

Recently Published Articles