No PriorsNo Priors Ep. 141 | With Sunday Robotics Co-Founders Tony Zhao and Cheng Chi
Sarah Guo and Tony Zhao on sunday Robotics Builds Cheap, Safe Home Robots With Massive Real-World Data.
In this episode of No Priors, featuring Tony Zhao and Sarah Guo, No Priors Ep. 141 | With Sunday Robotics Co-Founders Tony Zhao and Cheng Chi explores 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.
At a glance
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 ideasData-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 quotesWe’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
QUESTIONS ANSWERED IN THIS EPISODE
5 questionsHow will Sunday ensure robots remain safe, trustworthy, and aligned with user expectations once they’re operating autonomously in messy, unpredictable homes?
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.
What are the most important open research questions in scaling imitation learning from 10 million trajectories to hundreds of millions or billions?
How might a large population of home robots change labor markets for domestic work, childcare, and eldercare over the next decade?
What standards or benchmarks should the industry adopt to honestly compare generalization and reliability across different robotics approaches and companies?
If simulation and world models eventually catch up, how might that change Sunday’s current emphasis on real-world glove-collected data?
EVERY SPOKEN WORD
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