Yuke Zhu  

 



  ![](/sites/default/files/person/yuke.jpg)

  

 Yuke Zhu received his master’s and Ph.D. degrees from Stanford. His Ph.D. thesis centers around closing the perception-action loop to make robot intelligence more generalized and applicable to less-controlled environments. His research lies at the intersection of robotics, machine learning, and computer vision. He develops computational methods of perception and control that give rise to intelligent robot behaviors. Through his work, he aspires to teach robots to understand and interact with the visual world around them. His expertise has gained attention from a variety of news outlets, leading tech institutions, and award organizations. His publications have won several awards and nominations, including the Best Conference Paper Award in ICRA 2019. His work has been covered by media, such as MIT Technology Review and Stanford News.

For more information, please visit his [personal website](http://ai.stanford.edu/~yukez/).



   Research Area(s)

[Computer Vision](/research-area/computer-vision)

[Robotics](/research-area/robotics)

 

 

  

 Main Field of Interest

[Artificial Intelligence and Machine Learning ](/research-area/machine-learning-artificial-intelligence)

 

  

 Google Scholar

[https://scholar.google.com/citations?user=mWGyYMsAAAAJ&hl=en](https://scholar.google.com/citations?user=mWGyYMsAAAAJ&hl=en)

 

  

 

 

 



 ### Publications

 

### 2025 

[SPOT: SE(3) Pose Trajectory Diffusion for Object-Centric Manipulation](/publication/2025-05_spot-se3-pose-trajectory-diffusion-object-centric-manipulation)

Cheng-Chun Hsu, [Bowen Wen](/person/bowen-wen), [Jie Xu](/person/jie-xu), [Yashraj Narang](/person/yashraj-narang), , [Yuke Zhu](/person/yuke-zhu), Joydeep Biswas, [Stan Birchfield](/person/stan-birchfield)



[ICRA 2025](https://2025.ieee-icra.org/)









[LongVILA: Scaling Long-Context Visual Language Models for Long Videos](/index.php/publication/2025-04_longvila-scaling-long-context-visual-language-models-long-videos)

[Yukang Chen](/index.php/person/yukang-chen), Fuzhao Xue, Dacheng Li, Qinghao Hu, [Ligeng Zhu](/index.php/person/ligeng-zhu), Xiuyu Li, Yunhao Fang, Haotian Tang, Shang Yang, Zhijian Liu, Ethan He, Hongxu Yin, [Pavlo Molchanov](/index.php/person/pavlo-molchanov), [Jan Kautz](/index.php/person/jan-kautz), Linxi Fan, [Yuke Zhu](/index.php/person/yuke-zhu), Yao Lu (Jason), [Song Han](/index.php/person/song-han)



<https://openreview.net/forum?id=wCXAlfvCy6>









[NVIDIA Isaac GR00T N1: An Open Foundation Model for Humanoid Robots](/publication/2025-03_nvidia-isaac-gr00t-n1-open-foundation-model-humanoid-robots)

[Yuke Zhu](/person/yuke-zhu), [Linxi "Jim" Fan](/person/linxi-jim-fan), NVIDIA GEAR Team













### 2021 

[Hierarchical Planning for Long-Horizon Manipulation with Geometric and Symbolic Scene Graphs](/publication/2021-05_hierarchical-planning-long-horizon-manipulation-geometric-and-symbolic-scene)

Yifeng Zhu, [Jonathan Tremblay](/person/jonathan-tremblay), [Stan Birchfield](/person/stan-birchfield), [Yuke Zhu](/person/yuke-zhu)



[ICRA 2021](https://www.ieee-icra.org/)









[Fast Uncertainty Quantification for Deep Object Pose Estimation](/publication/2021-05_fast-uncertainty-quantification-deep-object-pose-estimation)

Guanya Shi, Yifeng Zhu, [Jonathan Tremblay](/person/jonathan-tremblay), [Stan Birchfield](/person/stan-birchfield), [Fabio Ramos](/person/fabio-ramos), Anima Anandkumar, [Yuke Zhu](/person/yuke-zhu)



[ICRA 2021](https://www.ieee-icra.org/)









### 2020 

[Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning](/publication/2020-12_bongard-logo-new-benchmark-human-level-concept-learning-and-reasoning)

Weili Nie, [Zhiding Yu](/person/zhiding-yu), Lei Mao, Ankit B. Patel, [Yuke Zhu](/person/yuke-zhu), Anima Anandkumar



[Conference on Neural Information Processing Systems (NeurIPS) 2020 (Spotlight)](https://nips.cc/Conferences/2020)









[OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation](/publication/2020-08_ocean-online-task-inference-compositional-tasks-context-adaptation)

Hongyu Ren, [Yuke Zhu](/person/yuke-zhu), Jure Leskovec, Anima Anandkumar, Animesh Garg



[Conference on Uncertainty in Artificial Intelligence (UAI) 2020](http://auai.org/uai2020/)