Matt Strong

CS PhD student in Robotics and AI, Stanford University · NSF Graduate Research Fellow

I’m a PhD student in Computer Science at Stanford, advised by Monroe Kennedy and Jeannette Bohg. I work on data-efficient 3D vision and tactile sensing for robotics and on vision-only autonomous driving, funded by the NSF Graduate Research Fellowship.

Before Stanford, I was a software engineer at Microsoft. As an undergrad at CU Boulder, I did research in the HIRO group with Alessandro Roncone and the SBS lab with Wangda Zuo. I also received the Stanford Centennial Teaching Award (spotlight).

Selected Work

Real2Sem2Real: 3D Semantic Augmentations for Robot Learning from a Single Human RGB Demonstration

Matthew Strong*, Ankush K. Dhawan*, Julien Poffet, Giuse Pham, Jinho So, Monroe Kennedy III

In submission

Turns one human RGB video into thousands of robot demonstrations across a whole object category.

• Website
• arXiv

VisTacAlign: Co-Training Dexterous Policies on Tactile Human and Robot Demonstrations

Julien Poffet, Matthew Strong, Ankush Dhawan, Baiyu Shi, Shalika Neelaveni, Yujia Yuan, Zhenan Bao, Monroe Kennedy III

arXiv 2026

Co-trains dexterous policies on human and robot demonstrations by aligning vision, hand motion and touch.

• Website
• arXiv

J-PARSE: Jacobian-based Projection Algorithm for Resolving Singularities Effectively in Inverse Kinematic Control of Serial Manipulators

Shivani Guptasarma, Matthew Strong, HongHao Zhen, Monroe Kennedy III

T-RO 2026

Smooth inverse kinematics near singularities, so manipulators can use more of their workspace in servoing, teleoperation and learning.

• Website
• arXiv

Learning to Drive is a Free Gift: Large-Scale Label-Free Autonomy Pretraining from Unposed In-The-Wild Videos

Matthew Strong, Wei-Jer Chang, Quentin Herau, Jiezhi Yang, Yihan Hu, Chensheng Peng, Wei Zhan

CVPR 2026

Pretrains a driving model on unlabeled, unposed videos, reaching state-of-the-art planning from a single front camera.

• Website
• arXiv
• IEEE

TensorTouch: Calibration of Tactile Sensors for High Resolution Stress Tensor and Deformation for Dexterous Manipulation

Won Kyung Do, Matthew Strong, Aiden Swann, Boshu Lei, Monroe Kennedy III

T-RO 2026

Calibrates optical tactile sensors to estimate stress tensors, deformation and force at pixel resolution.

• Website
• arXiv
• IEEE

DexFruit: Dexterous Manipulation and Gaussian Splatting Inspection of Fruit

Aiden Swann*, Alex Qiu*, Matthew Strong, Angelina Zhang, Samuel Morstein, Kai Rayle, Monroe Kennedy III

RA-L 2025

Gentle robotic handling of fragile fruit with optical tactile sensing, plus 3D damage inspection with Gaussian splats.

• Website
• arXiv
• IEEE

Next Best Sense: Guiding Vision and Touch with FisherRF for 3D Gaussian Splatting

Matthew Strong*, Boshu Lei*, Aiden Swann, Wen Jiang, Kostas Daniilidis, Monroe Kennedy III

ICRA 2025 Oral

Chooses where to look and touch next to train few-shot Gaussian splats, guided by FisherRF.

• Website
• arXiv
• IEEE

Touch-GS: Visual-Tactile Supervised 3D Gaussian Splatting

Aiden Swann*, Matthew Strong*, Won Kyung Do, Gadiel Sznaier Camps, Mac Schwager, Monroe Kennedy III

IROS 2024 Oral

Combines vision and touch to build Gaussian splats of hard scenes: few views, mirrors, transparent objects.

• Website
• arXiv
• IEEE

See all publications →