Vincent Liu

I’m building a robotics company to scale robot learning from humans in the real world. In the process of proofing this, I recently first-authored foundational research on zero-shot human-to-robot learning. I post about AI, robotics, and deep tech startups on X.

I received my BA in Mathematics and MS in Computer Science from Stanford University. My past work in AI has spanned theoretical and applied topics in 3D world models, self-driving, speech generation, multi-task learning, self-supervised learning, computer vision, reinforcement learning, and natural language processing.

news

Jun 03, 2025	We released our research on tactile robot learning from humans Feel the Force: Contact-Driven Learning from Humans 🤲
May 27, 2025	We released our research on zero-shot human-to-robot learning EgoZero: Robot Learning from Smart Glasses 📝

research

CSM Implicit Shape Foundation Model
October 2024

I helped train and scale CSM’s Implicit Shape Foundation Model, a 3D-native latent diffusion-Transformer model that produces 3D occupancy fields from single image in <1 minute, and its pre- and post-training data infrastructure. My work helped CSM land #1 on the 3D Arena Leaderboard and close initial enterprise contracts.

CSM Neural Radiance Field Foundation Model
March 2024

I helped train and scale CSM’s Neural Radiance Field (NeRF) Foundation Model, a 2D-native NeRF model that reconstructs 3D occupancy fields from single image in <1 minute. This model plugs into a slow refinement process based on Poole et al., 2022 and grew CSM to over 300k+ users. My work was presented at NVIDIA GTC 2024.

End-to-end Speech Synthesis with Generative Adversarial Networks
June 2022

I developed adversarial methods for end-to-end text-to-speech models as part of my Master’s thesis. I also lectured on these concepts for Stanford’s CS 236G: Generative Adversarial Networks Winter 2021 offering. Here are some samples from my implementation, which is an adaptation of Donahue et al., 2021; Kim et al., 2020.

Peter Piper picked a peck of pickled peppers

The quick brown fox jumps over the lazy dog

Tesla Full Self-Driving
September 2021

I trained and shipped models to the v9.2, v10.1, v10.2, and v10.3 releases of Tesla’s Full Self-Driving (FSD). My work was also featured in Tesla’s 2021 AI Day event.

NVIDIA Neural Modules
September 2020

I trained and shipped lightweight speech vocoders to the v1.0.0 release of NVIDIA’s Neural Modules (NeMo), an open-source speech AI toolkit for developers that has now expanded more broadly to conversational AI and LLMs.

publications

EgoZero: Robot Learning from Smart Glasses

Vincent Liu, Ademi Adeniji, Haotian Zhan, Raunaq Bhirangi, and 2 more authors

2025

Bib

@misc{liu2025egozerorobotlearningsmart,
  title = {EgoZero: Robot Learning from Smart Glasses},
  author = {Liu, Vincent and Adeniji, Ademi and Zhan, Haotian and Bhirangi, Raunaq and Abbeel, Pieter and Pinto, Lerrel},
  archiveprefix = {arXiv},
  year = {2025},
  eprint = {2505.20290},
  primaryclass = {cs.RO},
  url = {https://arxiv.org/abs/2505.20290}
}

Feel the Force: Contact-Driven Learning from Humans

Ademi Adeniji, Zhuoran Chen, Vincent Liu, Venkatesh Pattabiraman, and 4 more authors

2025

Bib

@misc{adeniji2025feelforcecontactdrivenlearning,
  title = {Feel the Force: Contact-Driven Learning from Humans},
  author = {Adeniji, Ademi and Chen, Zhuoran and Liu, Vincent and Pattabiraman, Venkatesh and Bhirangi, Raunaq and Haldar, Siddhant and Abbeel, Pieter and Pinto, Lerrel},
  archiveprefix = {arXiv},
  year = {2025},
  eprint = {2506.01944},
  primaryclass = {cs.RO},
  url = {https://arxiv.org/abs/2506.01944}
}

DABS 2.0: Improved Datasets and Algorithms for Universal Self-Supervision

Alex Tamkin, Gaurab Banerjee, Mohamed Owda, Vincent Liu, and 2 more authors

In Advances in Neural Information Processing Systems, 2022

Bib PDF Code

@inproceedings{NEURIPS2022_fa73aca7,
  author = {Tamkin, Alex and Banerjee, Gaurab and Owda, Mohamed and Liu, Vincent and Rammoorthy, Shashank and Goodman, Noah},
  booktitle = {Advances in Neural Information Processing Systems},
  editor = {Koyejo, S. and Mohamed, S. and Agarwal, A. and Belgrave, D. and Cho, K. and Oh, A.},
  pages = {38358--38372},
  publisher = {Curran Associates, Inc.},
  title = {DABS 2.0: Improved Datasets and Algorithms for Universal Self-Supervision},
  url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/fa73aca7b2af724fafbd4852957cd3e0-Paper-Datasets_and_Benchmarks.pdf},
  volume = {35},
  year = {2022},
}

Multivariate Group Robustness

Jupinder Parmar, Vincent Liu, and Tatsu Hashimoto

2022

Bib PDF Code

@misc{parmar2022multivariategrouprobustness,
  author = {Parmar, Jupinder and Liu, Vincent and Hashimoto, Tatsu},
  title = {Multivariate Group Robustness},
  year = {2022},
  url = {https://drive.google.com/file/d/1Qsd-REhXLNFzmJc83MN5pq4ZuaTH5K_v/view?usp=sharing},
}

End-to-end Speech Synthesis with Generative Adversarial Networks

Vincent Liu

Stanford University, 2022

Bib PDF Code

@misc{liu2022endtoendspeechsynthesis,
  author = {Liu, Vincent},
  title = {End-to-end Speech Synthesis with Generative Adversarial Networks},
  year = {2022},
  url = {https://drive.google.com/file/d/1HYZLzBwgmvs_oKf5tmTZdRWQLOul0blC/view?usp=sharing},
  school = {Stanford University},
  type = {mastersthesis}
}

DABS: a Domain-Agnostic Benchmark for Self-Supervised Learning

Alex Tamkin, Vincent Liu, Rongfei Lu, Daniel Fein, and 2 more authors

In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks, 2021

arXiv PDF Code

Recurrent Control Nets for Deep Reinforcement Learning

Vincent Liu, Ademi Adeniji, Nate Lee, Jason Zhao, and 1 more author

Stanford Undergraduate Research Journal, 2019

arXiv Bib Code

@article{liu2019recurrentcontrolnetsdeep,
  title = {Recurrent Control Nets for Deep Reinforcement Learning},
  author = {Liu, Vincent and Adeniji, Ademi and Lee, Nate and Zhao, Jason and Srouji, Mario},
  journal = {Stanford Undergraduate Research Journal},
  year = {2019},
  eprint = {1901.01994},
  archiveprefix = {arXiv},
  primaryclass = {cs.LG},
  url = {https://arxiv.org/abs/1901.01994},
}

Volumetric Semantic Segmentation of Glioblastoma Tumors from MRI Studies

Ademi Adeniji, and Vincent Liu

2019

Bib PDF Code

@misc{adeniji2019volumetricsegmentationtumor,
  author = {Adeniji, Ademi and Liu, Vincent},
  title = {Volumetric Semantic Segmentation of Glioblastoma Tumors from MRI Studies},
  year = {2019},
  url = {https://drive.google.com/file/d/11tmPV9PguRXKn-ZWsSea41p-Edo-U3DB/view},
}

Sequence-to-Sequence Generative Argumentative Dialogue Systems with Self-Attention

Ademi Adeniji, Nate Lee, and Vincent Liu

2019

Awarded Bib PDF Code Poster

CS 224N Best Custom Project

@misc{adeniji2019sequence,
  author = {Adeniji, Ademi and Lee, Nate and Liu, Vincent},
  title = {Sequence-to-Sequence Generative Argumentative Dialogue Systems with Self-Attention},
  year = {2019},
  url = {https://web.stanford.edu/class/archive/cs/cs224n/cs224n.1194/reports/custom/15844523.pdf},
}

teaching

CS 236G: Generative Adversarial Networks
March 2021

I helped create and teach CS 236G: Generative Adversarial Networks, which also became a Coursera course. I designed advanced material based on papers such as Pix2PixHD, GauGAN, SRGAN, BigGAN, and MUNIT. I created workshops (1, 2) that cover topics such as mode collapse, spectral normalization, orthogonal initialization, mixed precision, distributed training, Pytorch best practices, and also gave a guest lecture on adversarial methods in speech synthesis.