Eric Chan

@ericryanchan

Chief Scientist of Rhoda AI prev. PhD Student, Stanford University

ericryanchan.github.io

Joined December 2020

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Eric Chan
@ericryanchan
Mar 10
Today, we announce our team’s progress in pursuing a different type of foundation model for robotics: the Direct Video Action Model (DVA), which does our best to take robotics and turn it into a generative modeling problem we can scale. Technical blog:
Causal Video Models Are Data-Efficient Robot Policy Learners | Rhoda AI
From rhoda.ai
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Eric Chan
@ericryanchan
Sep 15
We recently came out with our blog post, Does Scaling Web-Video Pre-training Help Real Robots Do Real Work? This is a pretty exciting moment for robotics, because as far as I am aware, it is the first evidence that robot foundation models can scale on internet video data; not
Rhoda AI
@RhodaAI
Sep 15
At Rhoda, we care deeply about the science of pre-training for robotics. In one of the most rigorous studies of its kind, over thousands of trials and hundreds of hours of robot evaluations, we show how scaling web-video pre-training leads to better real-world robot performance.
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Eric Chan
@ericryanchan
Sep 11
An insightful study from @tongzhou_mu and the @RhodaAI team that examines how web video pre-training translates to robot performance. The gist: pre-training doesn’t just help; it scales, i.e. you can continue to reap greater benefits as you put more effort into pre-training. A
Tongzhou Mu 🤖🦾🦿
@tongzhou_mu
Sep 10
Does scaling pre-training on general web video improve a complex manipulation task in real deployment? We scale model size and pre-training compute, and test on one industrial task. Yes. The better a pre-trained model predicts web video, the better its post-trained policy. 🧵
Eric Chan
@ericryanchan
Jun 20
Delighted to have you on the team! Here’s to many new insights together :)
Vincent Sitzmann
@vincesitzmann
Jun 19
I am thrilled to join rhoda.ai @RhodaAI as an advisor, where I am helping harness the abilities of large-scale pre-training and video models for robotics, putting many of my lab's research learnings of the past few years into practice! I will be in-person at the
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Eric Chan
@ericryanchan
Mar 11
Vincent has been an inspiration for me since I started in AI — it's not an exaggeration that I wouldn't have done research at all if it were not for him. Thank you for the kind words!
Vincent Sitzmann
@vincesitzmann
Mar 10
These are very impressive results! The Rhoda team has decisively gotten "video models for robotics" to work. They train a generalist real-time, causal video model that they then quickly fine-tune using task-specific data to generate video plans (1/n)