About

Yen-Chen Lin

Research Scientist, NVIDIA

Email: yenchenl [at] nvidia (dot) com

Twitter / Google Scholar / Github

I am a research scientist at NVIDIA. I am interested in generative AI.
Previously, I was a Ph.D. student at MIT working with Phillip Isola and Alberto Rodriguez.

Selected Publications

For a full list of my publications, please see here.

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Cosmos World Foundation Model Platform for Physical AI

NVIDIA,

Arxiv 2025 / Video / Paper / Project Page / Github / HuggingFace
Press / BBC / Fortune / Wall Street Journal / Wired / New York Times / TechCrunch

Generative models for world simulation.

MIRA: Mental Imagery for Robotic Affordances

Lin Yen-Chen, Pete Florence, Andy Zeng, Jonathan T. Barron, Yilun Du, Wei-Chiu Ma, Anthony Simeonov, Alberto Rodriguez, Phillip Isola

CoRL 2022 / Video / Paper / Project Page

NeRF lets us synthesize novel orthographic views that work well with pixel-wise algorithms for robotic manipulation.

NeRF-Supervision: Learning Dense Object Descriptors from Neural Radiance Fields

Lin Yen-Chen, Pete Florence, Jonathan T. Barron, Tsung-Yi Lin, Alberto Rodriguez, Phillip Isola

ICRA 2022 / Video / Paper / Project Page / Slides / Code / Colab

Synthesizing correspondences data with Neural Radiance Fields (NeRF) for robots.

iNeRF: Inverting Neural Radiance Fields for Pose Estimation

Lin Yen-Chen, Pete Florence, Jonathan T. Barron, Alberto Rodriguez, Phillip Isola, Tsung-Yi Lin

IROS 2021 / Paper / Project Page

Performing differentiable rendering with Neural Radiance Fields (NeRF) enables object pose estimation and camera tracking.

Debiased Contrastive Learning

Ching-Yao Chuang, Joshua Robinson, Lin Yen-Chen, Antonio Torralba, Stefanie Jegelka

Neurips 2020 Spotlight / Paper / Code

A debiased contrastive objective that corrects for the sampling of same-label datapoints without knowledge of the true labels.

Learning to See before Learning to Act: Visual Pre-training for Manipulation

Lin Yen-Chen, Andy Zeng, Shuran Song, Phillip Isola, Tsung-Yi Lin

ICRA 2020 / Paper / Project Page / Google AI Blog

Transferring pre-trained vision models to perform grasping results in better sample efficiency and accuracy.

Experience-embedded Visual Foresight

Lin Yen-Chen, Maria Bauza, Phillip Isola

CoRL 2019 / Paper / Project Page

Meta-learning the video prediction models allows the robot to adapt to new objects' visual dynamics.

Omnipush: accurate, diverse, real-world dataset of pushing dynamics with RGBD images

Maria Bauza, Ferran Alet, Lin Yen-Chen, Maria Bauza, Tomas Lozano-Perez, Leslie P. Kaelbling, Phillip Isola Alberto Rodriguez,

IROS 2019 / Paper / Project Page

A dataset for meta-learning dynamic models. It consists of 250 pushes for each of 250 objects, all recorded with RGB-D camera and a high precision tracking system.

Tactics for Adversarial Attack on Deep Reinforcement Learning Agents

Yen-Chen Lin, Zhang-Wei Hong, Yuan-Hong Liao, Meng-Li Shih, Ming-Yu Liu, Min Sun

IJCAI 2017 / Paper / Project Page

Strategies for adversarial attacks on deep RL agents.

Deep 360 Pilot: Learning a Deep Agent for Piloting through 360° Sports Videos

Yen-Chen Lin*, Hou-Ning Hu*, Ming-Yu Liu, Hsien-Tzu Cheng, Yung-Ju Chang, Min Sun

CVPR 2017

Paper / Project Page

Oral Presentation

An agent that learns to guide users where to look in 360° sports videos.

Tell Me Where to Look: Investigating Ways for Assisting Focus in 360° Video

Yen-Chen Lin, Yung-Ju Chang, Hou-Ning Hu, Hsien-Tzu Cheng, Chi-Wen Huang, Min Sun

CHI 2017

Paper / Project Page

A study about how to assist users when watching 360° videos.