No Priors Ep. 141 | With Sunday Robotics Co-Founders Tony Zhao and Cheng Chi

The robotics industry is on the cusp of its own “GPT” moment, catalyzed by transformative research advances. Enter Memo, the first general-intelligence personal robot, focused on taking on your chores to give back your time. Sarah Guo sits down with Tony Zhao and Cheng Chi, co-founders of Sunday Robotics, to discuss the state of AI robotics. Tony and Cheng speak to the challenges they faced while developing their technology, the innovative glove system employed to scale real-world data collection, and the impact of diffusion policy and imitation learning. Plus, they talk about their 2026 in-home beta program and why personal robots are only a handful of years away from mass deployment. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @tonyzzhao | @chichengcc | @sundayrobotics Chapters: 00:00 – Tony Zhao and Cheng Chi Introduction 00:56 – State of AI Robotics 02:11 – Deploying a Robot Pre-AI 03:13 – Impact of Diffusion Policy 04:29 – Role of ACT and ALOHA 07:02 – Imitation Learning - Enter UMI 10:38 – Introducing Sunday 11:57 – Sunday’s Robot Design Philosophy 15:05 – Sunday’s Shipping Timeline 19:02 – Scale of Sunday’s Training Data 23:58 – Importance of Data Quality at Scale 24:56 – Technical Challenges 27:59 – When Will People Have Home Robots? 30:48 – Failures of Past Demos 32:34 – Sunday’s Demos 36:53 – What Sunday’s Hiring For 39:10 – Conclusion

Tony ZhaoguestSarah GuohostCheng Chiguest

Nov 18, 202539mWatch on YouTube ↗

WHAT IT’S REALLY ABOUT

Sunday Robotics Builds Cheap, Safe Home Robots With Massive Real-World Data

Sunday Robotics co-founders Tony Zhao and Cheng Chi describe how recent advances in imitation learning, diffusion policies, and low-cost hardware are finally enabling general-purpose home robots. They explain the shift from brittle, hand-engineered classical robotics to scalable, data-driven systems powered by millions of real-world trajectories collected via custom gloves and tools like Umi. The founders emphasize full-stack integration—hardware, data collection, training pipelines, and product design—to achieve dexterity, generalization, and safety at consumer price points. They outline a 2026 home beta program, discuss realistic timelines and costs, and explain how to critically interpret robotics demos in an industry full of hype.

IDEAS WORTH REMEMBERING

5 ideas

Data-driven imitation learning is finally making general-purpose home robots viable.

Diffusion policies and transformer-based models trained on paired observation–action data have overcome the brittleness of classical, hand-engineered pipelines, allowing robots to learn complex manipulation from demonstrations at scale.

Scalable, high-quality data collection is the core bottleneck in robotics progress.

Sunday’s custom glove and Umi gripper setups let hundreds of non-expert humans collect millions of diverse trajectories in real homes, turning three students’ efforts into one of the largest real-world robotics datasets.

Full-stack integration is a competitive advantage in robotics.

Building hardware, data collection tools, cleaning pipelines, and training recipes under one roof lets Sunday iterate rapidly, fix failure modes end-to-end, and avoid research directions that don’t scale to real products.

Robot hardware can be cheaper, softer, and less precise when paired with strong perception.

Because modern robots ‘have eyes,’ low-cost, compliant actuators can rely on AI to correct mechanical inaccuracies in real time, enabling safe, home-ready systems without industrial-grade precision motors.

Data quality and distribution matching matter as much as data volume.

Failures like a robot trained only on rainy data breaking under direct sunlight show that broad generalization requires careful coverage of real-world conditions plus automated monitoring, calibration, and filtering pipelines.

WORDS WORTH SAVING

5 quotes

We’re kind of in between the GPT moment and the ChatGPT moment for robotics.

— Tony Zhao

In order for the robot to work in a sunny environment, it must have seen sunny environments in the training data.

— Cheng Chi

We really think the robot should have a face. It should have a cute face, and it should be very friendly.

— Tony Zhao

Data quality really matters. I always knew it, but once you scale it up, it really matters.

— Cheng Chi

When we look at demos, only index on things that are shown, and that’s likely the full scope of that task.

— Tony Zhao

State of AI robotics and the shift from classical to data-driven methodsImitation learning, diffusion policies, ALOHA, ACT, and Umi for scalable data collectionSunday’s full-stack approach: custom gloves, hardware design, and training pipelinesDesigning a general-purpose home robot: cost, safety, dexterity, and form factorScaling real-world robot data: quality control, distribution matching, and operationsDebate over data scaling methods: teleoperation, gloves, RL, simulation, world modelsRoadmap and demos: home beta program, generalization across homes, and reliability

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