Eric Chan

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@ericryanchan

Chief Scientist of Rhoda AI

prev. PhD Student, Stanford University

ericryanchan.github.io Joined December 2020

110 Following 939 Followers 84 Posts

Latest posts 5

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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: rhoda.ai/research/direc…

www.rhoda.ai Causal Video Models Are Data-Efficient Robot Policy Learners | Rhoda AI At Rhoda AI, we are building towards generalist robotics. Our Direct Video-Action Model (DVA) reformulates robot policies as video generation, unlocking data-efficient task learning, scaling,...

Eric Chan @ericryanchan Nov 3

The best systems are the ones that are as simple as possible.

In this case, we perform mobile manipulation with a single DVA. No separate mobility or navigation stack; just pure joint prediction of both how the arms move and how the robot maneuvers around novel obstacles.

Impressive work, @joannetruong!

Rhoda AI @RhodaAI Nov 3

We taught a robot how to move around in only 15 minutes.

That’s all the mobility data our Direct Video-Action-powered robot needed to accomplish a task that requires mobility and manipulation.

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Eric Chan @ericryanchan Oct 19

Excited to share @BaadeAlan's latest work: a new approach to representation learning designed specifically for generative modeling!

Semantic representations like DINO have become remarkably effective tokenizers for image and video generation. But DINO was never designed for this task. Its success in generative modeling is almost accidental. What if we learned representations specifically to make generation easier?

Alan Baade Verified account @BaadeAlan Oct 19

1/ The tokenizer is a new scaling axis!

We introduce an approach for diffusable representation learning that's principled, scalable, and works when DINO/LeJEPA collapse.

We get joint tokenizer+diffusion scaling laws and it's compute-optimal vs raw diffusion

w/ @drfeifei @GordonWetzstein @jcjohnss @ericryanchan @vincesitzmann @KyleSargentAI

Thread, Paper, and Blogpost below

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Eric Chan @ericryanchan Oct 14

I often get asked how world models will integrate tactile data, when they’re trained primary from video.

Here’s a simple solution that works surprisingly well: embed the tactile information into the video stream, as pixels. At Rhoda, we’ve developed inexpensive and sensitive tactile sensors, and have demonstrated their capabilities, including allowing a blind robot to sense. It’s an exciting move towards greater dexterity!

Rhoda AI @RhodaAI Oct 14

What can a robot do without vision?

We gave our robot a sense of touch and showed it can recognize and retrieve objects with its cameras blindfolded.

Introducing Rhoda’s new tactile sensor—it feels pressure, texture, vibration, slip, and temperature, just like your fingertips.

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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 merely on teleoperation, UMI data, or egocentric data, but on the data you can truly find anywhere.

It’s the necessary requirement for reaching massively powerful models.

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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