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"A worshiper of science,
a slave to freedom."

Dai, Yinlong
戴尹龙

Hello! My name is Yǐnlóng DAI. I am a PhD student at Virginia Tech. I am currently working on human robot interaction in the Collaborative Robotics Lab (Collab) directed by Dr. Dylan Losey.

Prior to this, I received my Bachelor's degree with Honors in Computer Science and a minor in Robotics from New York University. I was an undergraduate research assistant at the Generalizable Robotics and AI Lab (GRAIL) under the supervision of Prof. Lerrel Pinto, where I worked on robotic dexterous manipulation. I was also fortunate to work with Prof. Gizem Kayar on surgical simulation and Prof. Qi Lei on medical image classification.

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  • 2024.5   🎓I graduated from New York University.

Research Icon Research

I'm interested in robotics, computer vision and reinforcement learning. I work at the intersection of Machine learning and Robotics.

OPEN TEACH: A Versatile Teleoperation System for Robotic Manipulation
Aadhithya Iyer, Zhuoran Peng, Yinlong Dai, Irmak Guzey, Siddhant Haldar, Soumith Chintala, Lerrel Pinto
CORL, 2024
project code / arXiv

A new teleoperation system leveraging VR headsets to immerse users in mixed reality for intuitive robot control.

See to Touch: Learning Tactile Dexterity through Visual Incentives
Irmak Guzey, Yinlong Dai, Ben Evans, Soumith Chintala, Lerrel Pinto
ICRA, 2024
project code / arXiv

In this work, we present Tactile Adaptation from Visual Incentives (TAVI), a framework that enhances tactile-based dexterity by optimizing dexterous policies using vision-based rewards.

Exploring Minimally Sufficient Representation in Active Learning through Label-Irrelevant Patch Augmentation
Zhiyu Xue, Yinlong Dai, Qi Lei
Conference on Parsimony and Learning, PMLR, 2024
PDF

In this work, we intend to learn a "good" feature representation that is both sufficient and minimal, facilitating effective AL for medical image classification.

Laughing Icon Reading

Science fiction helps us navigate the future. I found these short stories interesting:


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