Russ Tedrake

@RussTedrake

Co-Founder and CEO at Walden Robotics. Professor at MIT.

locomotion.csail.mit.edu/russt.html Joined July 2022

99 Following 6.9K Followers 36 Posts

Latest posts 5

Russ Tedrake @RussTedrake Aug 26

Thanks for hosting me, Brad.

Brad Porter Verified account @BradPorter_ Aug 25

The best case for wheels I've heard came from the guy who has taught legged locomotion at MIT for 20 years.

AMR safety cases assume you slow proportional to distance and come to rest. A balancing biped may need to take steps to stop. The safety zone explodes and the robot ends up behind a fence.

A robot behind a fence isn't deployed.

Deployed EP12 is live: cobot-deployed.com/ep12

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Russ Tedrake @RussTedrake Aug 21

When we first started working on Diffusion Policy, which felt like an "unlock" for fitting human demonstration data, the explanation I gave myself was that "human demonstration data was subtly much more multimodal than we thought, so we need the modeling power of denoising diffusion to fit it properly."

What Michael's paper brings to light is that some of this multimodality was probably exacerbated (more than I realized) by the fact that we've only been giving our models incredibly short observation context lengths.

For me, this is pretty compelling evidence that we should be pushing for policies with long-context lengths and short action prediction horizons.

Michael Zeng Verified account @michzeng_ Aug 20

Action chunking — especially executing long action sequences open-loop — is widely used in imitation learning for robotic manipulation. Why is it so effective and do we really need it? We find a key reason:

Long open-loop execution helps short-context policies imitate non-Markovian experts.

With this insight, we show how to move beyond open-loop execution: extending policy context restores reactivity while achieving even higher task performance.

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Russ Tedrake @RussTedrake Jul 15

Thanks to @HeySamantha and @Bloomberg for covering our launch bloomberg.com/news/articles/…

www.bloomberg.com Toyota-Backed Startup Walden Robotics Comes Out of Stealth With $1.1 Billion Valuation Walden Robotics, a startup working on humanoids that was spun out of a Toyota robotics research lab, has secured approximately $300 million in funding as it emerges from stealth with a valuation of...

Russ Tedrake @RussTedrake Jul 15

In January, I started "building something new" with an incredible team. Today I finally get to share some first details about what we've been building. We've called it Walden Robotics (waldenrobotics.com).

I thought long and hard about my own reasons for starting this company. It's not only about the robots. It's also about people. I've tried to capture those thoughts in my first Walden blog post: waldenrobotics.com/news/why-walden

It's been an incredible ride so far. Within just a few months of forming the company, we were already operating a general-purpose robot with an end-to-end policy in production in one of the most important factories in North America. It's amazing at how much I've already learned from that experience. There is a lot of work to do, but the mission has never been so clear.

Please help me welcome Walden Robotics into the world. And stay tuned for more updates!

youtube.com/watch?v=fewvZr…

www.waldenrobotics.com Walden Robotics | Build the Future of Physical AI with Us We're ramping up fast with a world-class team and hiring broadly across robotics, AI, operations, product, and business.

Russ Tedrake @RussTedrake Jun 20

When it comes to diffusion policies, VLAs, and LBMs, we all know that data quality matters. Many of us have spent considerable time filtering data, and discarding large amounts of data that might not be considered to be sufficiently high quality. This feels very inefficient.

We’ve seen several proposals of late for how we might learn from suboptimal data. pi0.7, for instance, suggested that we learn models conditioned on the data quality.

Adam’s new paper exposes a different (complementary) angle. When we’re training denoising diffusion models, each data point is potentially used several times — at different noise levels. **The value of a data point might be different depending on the noise level.**

Adam’s work explores this nicely and shows that he can extract value even out of significant portions of the OXE dataset that many of us had previously discarded as suboptimal.

Definitely worth having a look.

Adam Wei @CoRL ✈️ Verified account @adamwei_ Jun 18

🤖 We introduce Ambient Diffusion Policy, a simple and principled method for training policies with suboptimal data in robotics.

Suboptimal data is everywhere in robotics…
❌ Data filtering is wasteful
❌ Co-training learns both good and bad features
✅ Ambient Diffusion Policy selectively learns useful features via noise-dependent data usage

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