yifanhou at stanford dot edu
I am a Postdoctoral researcher at the REALab, Stanford University, working with Prof. Shuran Song. Prior to joining Stanford, I was an Applied Scientist on the Vulcan team at Amazon Robotics. I obtained my PhD and MS degrees from the Robotics Institute at Carnegie Mellon University, advised by Prof. Matthew T. Mason. I obtained BoE from the department of Automation at Tsinghua University. I had also spent time at Toyota Research Institute and MIT.
My career goal is to achieve human level manipulation dexterity on robots. During my PhD, I designed methods to execute contact-rich manipulation robustly against modeling uncertainties and disturbance forces using active compliance control schemes. I then spent three years at Amazon Robotics pushing compliant manipulation from research to industrial products in the Stow project. I believe that robotic manipulation has an opportunity of wide adoption in people’s daily life, where the bottleneck is the ability to scale up the aquisition of robust, general manipulation skills. Currently my research interests include the intersection of data-driven visual motor policies and robust model-based control as well as dexterous manipulation.
news
| Jan 25, 2026 | Checkout UMI-FT, our solution for force-aware, scalable compliance manipulation. |
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| Sep 29, 2025 | DexUMI was selected as a best paper finalist at CoRL 2025! |
| Sep 27, 2025 | CR-DAgger won the best paper award at the CoRL 2025 Human to Robot (H2R) workshop! |
| Aug 10, 2025 | DexUMI and Vision in Action are accepted to CoRL 2025. See you at Seoul! |
| May 25, 2025 | I gave an invited talk “Empower Robot Learning with Model-based Manipulation” at the Beyond Pick and Place workshop at ICRA 2025. Recording is available here. |
| May 21, 2025 | Adaptive Compliance Policy won the best paper award at the ICRA 2025 Contact-rich Manipulation workshop. |
| Feb 19, 2025 | I gave a lecture on “Introduction to Compliance Control” at Stanford EE/CS 381. Slides are available here. |
| Jan 17, 2025 | My invited talk at the Stanford Robotics Seminar was released. This talk is an overview of my work on compliant manipulation. |
| Jun 26, 2024 | Our paper simPLE: a visuotactile method learned in simulation to precisely pick, localize, regrasp, and place objects has been published at Science Robotics. |
selected publications
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In-the-Wild Compliant Manipulation with UMI-FT
Hojung Choi*, Yifan Hou*, Chuer Pan, Seongheon Hong, Austin Patel, Xiaomeng Xu, Mark R. Cutkosky, and Shuran Song
2026
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Compliant Residual DAgger: Improving Real-World Contact-Rich Manipulation with Human Corrections
2025
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DexMachina: Functional Retargeting for Bimanual Dexterous Manipulation
Mandi Zhao, Yifan Hou, Dieter Fox, Yashraj Narang, Ajay Mandlekar, and Shuran Song
2025
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DexUMI: Using Human Hand as the Universal Manipulation Interface for Dexterous Manipulation
Mengda Xu*, Han Zhang*, Yifan Hou, Zhenjia Xu, Linxi Fan, Manuela Veloso, and Shuran Song
2025
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Adaptive Compliance Policy: Learning Approximate Compliance for Diffusion Guided Control
Yifan Hou, Zeyi Liu, Cheng Chi, Eric Cousineau, Naveen Kuppuswamy, Siyuan Feng, Benjamin Burchfiel, and Shuran Song
In 2025 IEEE International Conference on Robotics and Automation (ICRA), 2025
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SimPLE, a visuotactile method learned in simulation to precisely pick, localize, regrasp, and place objects
Maria Bauza, Antonia Bronars, Yifan Hou, Ian Taylor, Nikhil Chavan-Dafle, and Alberto Rodriguez
Science Robotics, 2024
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Manipulation with Shared Grasping
Yifan Hou, Zhenzhong Jia, and Matthew T Mason
Jun 2020
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Robust execution of contact-rich motion plans by hybrid force-velocity control
Yifan Hou and Matthew T Mason
In 2019 International Conference on Robotics and Automation (ICRA), Jun 2019
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Fast planning for 3d any-pose-reorienting using pivoting
Yifan Hou, Zhenzhong Jia, and Matthew T Mason
In 2018 IEEE International Conference on Robotics and Automation (ICRA), Jun 2018