MOUNTAIN VIEW, Calif., Nov. 19, 2025 (GLOBE NEWSWIRE) — Sunday, an AI startup founded by Stanford PhD roboticists Tony Zhao (CEO) and Cheng Chi (CTO), today emerges from stealth to introduce Memo. Memo is a personal robot engineered to help families gain back time spent on chores like dishes, laundry, and tidying up. Its development centers on learning from authentic daily routines collected in more than 500 real homes using Sunday’s patented Skill Capture Glove, a wearable that records how people move, clean, and organize.
Memo puts household data at the heart of personal robotics. Unlike most in-home robots, Memo’s training comes from approximately 10 million episodes of genuine household routines, representing an unmatched level of data diversity, quality, and in-the-wild volume. This advantage enables Memo to adapt to the unpredictability found in kitchens, living rooms, and laundry spaces, mastering “long horizon” tasks like clearing the table, running the dishwasher, folding clothes, putting away shoes, and brewing espresso.
“The problem has always been data. Most home robots start as adaptations of industrial machines, and those trained in labs rarely succeed in unpredictable, real-world environments,” said Tony Zhao, co-founder and CEO of Sunday. “Our Skill Capture Glove changes this by collecting thousands of hours of daily routines from hundreds of families. That practical knowledge lets Memo develop the skills families truly care about. We built Memo to give people back time for what matters, with the safety needs for any family in mind. This is a turning point for home robotics.”
Memo is built for homes, with safety and stability as priorities. Instead of a bipedal humanoid shape, Memo features a rolling base for greater balance and lower weight. In the rare event of power loss, Memo remains stable, avoiding the risk of falls. Its silicone-clad design is soft, approachable, and built to fit comfortably into family living spaces.
Starting November 19th, 2025, Sunday will accept applications for Memo’s Founding Family Beta, launching in late 2026. Fifty households will become early adopters, receiving individually numbered robots along with direct support and the opportunity to guide future capabilities.
“The promise of AI robotics isn’t back-flipping or dancing demos, but robots that work in messy, real-world situations. To have those, we need real-world training data. We have about one-millionth of the data we need,” said Eric Vishria, General Partner at Benchmark. “Tony and Cheng’s approach finally makes collecting robot-ready data at a massive scale possible. Their breakthroughs mark the start of an exponential curve toward a future where robots actually work in our day-to-day lives.”
About Sunday
Sunday is a Mountain View–based robotics startup founded by Tony Zhao and Cheng Chi. Zhao and Chi’s research includes ALOHA, transformer adaptation for robotics, and diffusion policy—their breakthroughs enabled robots to finally learn by watching humans. The team has grown to 25 engineers and researchers and brings experience from Stanford, Tesla, DeepMind, Waymo, Meta, and Neuralink.
About Benchmark
Benchmark is an early-stage venture capital firm investing in AI, open-source, marketplaces, infrastructure and enterprise software. Based in San Francisco and Woodside, Benchmark works alongside entrepreneurs building startups into transformational companies.
Benchmark’s $425 million fund is comprised of equal general partners who take board seats and a hands-on approach to every entrepreneur and company we back, with no junior investment staff.
Benchmark’s current portfolio of early-stage venture investments includes AI-native companies such as Cerebras, Cursor, DeepCogito, Fireworks AI, Greptile, HeyGen, LangChain, Legora, Manus AI, Reducto and Sema4.ai; public companies such as Asana, Amplitude, Confluent, Elastic, New Relic, Nextdoor, Stitch Fix, Snap, and Uber; and Fortune 500 companies from Juniper to eBay to Red Hat.
A video accompanying this announcement is available at https://www.globenewswire.com/NewsRoom/AttachmentNg/cffe6e43-6cf2-41f8-90a1-5ee7f3c7a0c3
