Shiwei Sheng

Robot learning research @ | ex-Nvidia | ex-Waymo

[Shiwei Sheng @ShiweiShengR]

Three years ago, when I witnessed the breakthrough of LLMs, I became convinced that a similar inflection point would eventually arrive in robotics.

Over the past several years of robotics research, I’ve learned that a single overfit demo will not reveal robotics’ “GPT moment”—and that even many impressive demos do not add up to physical AGI. What we need is a robot brain that can perform with human-level quality and robustness across the long tail of real-world situations.

That was why I subscribed to ChatGPT three years ago: it was the first time AI felt broadly useful, not merely impressive.

Now comes ACT-2—a meaningful step toward bringing the same transition to robotics: from demos to generalization, from isolated capabilities to scalable intelligence, and from impressive videos to systems people can actually rely on.

[Tony Zhao @tonyzzhao]

This fall, ACT-2’s first Solve enters homes through our Beta Program, the final step towards fully autonomous home robot deployment.

Full technical report: https://t.co/oVR2TAdlHg

[Cheng Chi @chichengcc]

We just hit a weird milestone: our model became more reliable than your average home WiFi.

Just like everybody else, we thought cloud inference was the obvious choice. Yet 2 days into the ACT-2 eval, our mind completely changed.

If our hero @ArpitKalla didn’t cook, this video wouldn’t exist 🧵

[Tony Zhao @tonyzzhao]

Sunday always prioritizes rigor and concrete progress towards our north star. Consistency is how we win.

[Junyao Shi @JunyaoShi]

I came from a world where I measured robotics research in days: collect hundreds of demonstrations, train, then watch the policy break as soon as the setup changed.

Since I joined Sunday, I’ve watched that loop compress dramatically.

ACT-2 can learn a new folding strategy from one demonstration, with just five minutes of fine-tuning, then transfer it to unseen garments and beds. What once took a week can now happen in an hour.

That’s the deeper impact of scaling pretraining: it doesn’t just produce a better policy. It produces a better learner.

[Sunday @sundayrobotics]

Introducing ACT-2 Preview, the world’s first robotics model that works in your home.

99% success rate, fully autonomous in unseen homes. Zero data from you.

[Tony Zhao @tonyzzhao]

The poll results are obvious.
We’re heads down now. 7/17

[Keval Shah @kevalshah01]

The inbound from new robotics data vendors has been nonstop since last November. Half the companies that messaged us back then have since pivoted to something else. Collecting high quality robotics data is just brutally hard, and the market keeps proving it

The data ops team @sundayrobotics is genuinely world-class, led by the one and only @perryzjia. Come join us, we’re hiring :)

[Tony Zhao @tonyzzhao]

Just saw the craziest emergent behavior I’ve ever seen in robotics. The team is absolutely losing it. We haven’t posted in a while… should we write it up?

[Junyao Shi @JunyaoShi]

Personal update: I've joined @sundayrobotics.

Two questions ran through my whole PhD: how to learn from scalable human data, and how to build general-purpose robots.

Trying to answer them convinced me of one thing: general-purpose robots will never come from better models alone. It takes tight iteration across data, hardware, model, control, and evaluation. Every loop you can shorten matters.

My first dinner with @tonyzzhao and @chichengcc turned into a four-hour conversation. I walked away realizing how much we saw eye to eye: scale the data, think full-stack, start from the problem you want to solve instead of the idea you want to win.

So getting to work at Sunday is a dream come true, a place to solve generalization with the full breadth of human data and system-level thinking, and keep chasing the questions I care most about.

After my first month in, two things stand out: Sunday’s full-stack team iterates unbelievably fast, and the energy when everyone is aligned on the same vision is electric. This speed and energy is exactly why what used to feel impossible now feels close.

Home robots, the frontier physical AI in the hands of ordinary people, were long seen as a distant dream . At Sunday, I watch this dream take shape every day. I'm convinced there's real research-market fit here: foundation models and home robots point toward the same north star, generalization, not specialization, because every home is different.

Excited for the zero-to-one moment ahead.

[Cheng Chi @chichengcc]

Proud of what you and the team have achieved @johnson_zhsh! More to come