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Tony Zhao

@tonyzzhao • 148,983 subscribers

Co-founder and CEO @sundayrobotics. Stanford PhD dropout, ex Deepmind, Tesla, GoogleX

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China's progress in humanoid robots deserves more attention. The video below has <300 views on YouTube, while the robot appears to be - more agile than Tesla's Optimus - more dexterous than Agility's Digit - (likely) a lot cheaper than both

China's progress in humanoid robots deserves more attention. The video below has <300 views on YouTube, while the robot appears to be - more agile than Tesla's Optimus - more dexterous than Agility's Digit - (likely) a lot cheaper than both

448,558 次观看

Specifically, wine glass loading is the most delicate subtask: Push down with too much force? Shatter. Insert the wrong prong? Shatter. We broke many during development, but zero over 20+ live demo sessions.

Specifically, wine glass loading is the most delicate subtask: Push down with too much force? Shatter. Insert the wrong prong? Shatter. We broke many during development, but zero over 20+ live demo sessions.

52,705 次观看

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Introducing ACT-2 Preview The first robotics model to unify broad generalization with high reliability. A single fine-tuning example can teach Memo a new behavior that generalizes. Zero shot, real unseen homes, 99% success rate.

Introducing ACT-2 Preview The first robotics model to unify broad generalization with high reliability. A single fine-tuning example can teach Memo a new behavior that generalizes. Zero shot, real unseen homes, 99% success rate.

1,042,949 次观看 • 1 个月前

Today, we present a step-change in robotic AI Sunday. Introducing ACT-1: A frontier robot foundation model trained on zero robot data. - Ultra long-horizon tasks - Zero-shot generalization - Advanced dexterity 🧵->

Today, we present a step-change in robotic AI Sunday. Introducing ACT-1: A frontier robot foundation model trained on zero robot data. - Ultra long-horizon tasks - Zero-shot generalization - Advanced dexterity 🧵->

2,056,441 次观看 • 9 个月前

We found a general recipe for Solves: scale pretraining, then hill-climb with minimal in-house data. For the first time, one fine-tuning example can teach a new behavior that generalizes. Below: 4 folding strategies, learned from one example each and tested on held-out setups.

We found a general recipe for Solves: scale pretraining, then hill-climb with minimal in-house data. For the first time, one fine-tuning example can teach a new behavior that generalizes. Below: 4 folding strategies, learned from one example each and tested on held-out setups.

170,577 次观看 • 1 个月前

The table-to-dishwasher task is the classic nightmare scenario for roboticists: Long-horizon, highly dexterous, precise, whole-body manipulation combined with delicate, transparent, reflective, and deformable objects. Yet Memo handles it so naturally and elegantly.

The table-to-dishwasher task is the classic nightmare scenario for roboticists: Long-horizon, highly dexterous, precise, whole-body manipulation combined with delicate, transparent, reflective, and deformable objects. Yet Memo handles it so naturally and elegantly.

802,704 次观看 • 9 个月前

We raised $165M at a $1.15B valuation to stop doing demos. 2026 is about 1) deployment and 2) research. We will start shipping Memo with our new frontier models in a few months. Our series-B is led by Coatue, with Thomas Laffont joining the board. ->🧵

We raised $165M at a $1.15B valuation to stop doing demos. 2026 is about 1) deployment and 2) research. We will start shipping Memo with our new frontier models in a few months. Our series-B is led by Coatue, with Thomas Laffont joining the board. ->🧵

454,410 次观看 • 6 个月前

Introducing 𝐌𝐨𝐛𝐢𝐥𝐞 𝐀𝐋𝐎𝐇𝐀🏄 -- Hardware! A low-cost, open-source, mobile manipulator. One of the most high-effort projects in my past 5yrs! Not possible without co-lead Zipeng Fu and Chelsea Finn. At the end, what's better than cooking yourself a meal with the 🤖🧑‍🍳

Introducing 𝐌𝐨𝐛𝐢𝐥𝐞 𝐀𝐋𝐎𝐇𝐀🏄 -- Hardware! A low-cost, open-source, mobile manipulator. One of the most high-effort projects in my past 5yrs! Not possible without co-lead Zipeng Fu and Chelsea Finn. At the end, what's better than cooking yourself a meal with the 🤖🧑‍🍳

1,668,965 次观看 • 2 年前

The most insane gait I've seen on a humanoid. Walking with locked knees is much more energy efficient as the motors don't need to be engaged all the time. Public info of EngineAI: - team of 36 - raised ~14M USD - investment from SenseTime, Hefei province - founded Oct 2023

The most insane gait I've seen on a humanoid. Walking with locked knees is much more energy efficient as the motors don't need to be engaged all the time. Public info of EngineAI: - team of 36 - raised ~14M USD - investment from SenseTime, Hefei province - founded Oct 2023

420,447 次观看 • 1 年前

One less-known fact about glove-based data collection: it produces higher quality data than teleop on contact-rich tasks. Remote teleop can’t provide good force feedback, but gloves do naturally, making tasks like sock folding, which rely on feel, far easier to capture.

One less-known fact about glove-based data collection: it produces higher quality data than teleop on contact-rich tasks. Remote teleop can’t provide good force feedback, but gloves do naturally, making tasks like sock folding, which rely on feel, far easier to capture.

186,027 次观看 • 9 个月前

Introducing 𝐀𝐋𝐎𝐇𝐀 𝐔𝐧𝐥𝐞𝐚𝐬𝐡𝐞𝐝 🌋 - Pushing the boundaries of dexterity with low-cost robots and AI. Google DeepMind Finally got to share some videos after a few months. Robots are fully autonomous filmed in one continuous shot. Enjoy!

Introducing 𝐀𝐋𝐎𝐇𝐀 𝐔𝐧𝐥𝐞𝐚𝐬𝐡𝐞𝐝 🌋 - Pushing the boundaries of dexterity with low-cost robots and AI. Google DeepMind Finally got to share some videos after a few months. Robots are fully autonomous filmed in one continuous shot. Enjoy!

354,443 次观看 • 2 年前

This property allows us to hill-climb performance in our office, and trust those gains to hold in unseen homes Our fleet of Memos runs in parallel to rapidly advance reliability, quality, and speed. Left: fleet-scale improvement in-house Right: Memo working across unseen homes

This property allows us to hill-climb performance in our office, and trust those gains to hold in unseen homes Our fleet of Memos runs in parallel to rapidly advance reliability, quality, and speed. Left: fleet-scale improvement in-house Right: Memo working across unseen homes

32,081 次观看 • 1 个月前

Robots are not ready to take over the world yet! Zipeng Fu and I just compiled a video of the dumbest mistakes 𝐌𝐨𝐛𝐢𝐥𝐞 𝐀𝐋𝐎𝐇𝐀🏄 made in the autonomous mode 🤣 We are also planning to organize some live demos after taking a break. Stay tuned!

Robots are not ready to take over the world yet! Zipeng Fu and I just compiled a video of the dumbest mistakes 𝐌𝐨𝐛𝐢𝐥𝐞 𝐀𝐋𝐎𝐇𝐀🏄 made in the autonomous mode 🤣 We are also planning to organize some live demos after taking a break. Stay tuned!

333,034 次观看 • 2 年前

It is even more fun to see how Memo reacts to unseen environments. We deploy it to 6 unseen Airbnbs and task the robot with fine-grained tasks such as picking up utensils from the plate. Because we train on data from over 500 homes, the new home is instantly familiar to Memo.

It is even more fun to see how Memo reacts to unseen environments. We deploy it to 6 unseen Airbnbs and task the robot with fine-grained tasks such as picking up utensils from the plate. Because we train on data from over 500 homes, the new home is instantly familiar to Memo.

111,791 次观看 • 9 个月前

Laundry is our first Solve of many. Our recipe is so general that scaling data and compute gives us predictable improvements. Unlocking one Solve accelerates the next Solve. The same ACT-2 model is learning to vacuum, organize toys, zip clothing, and turn pants inside out.

Laundry is our first Solve of many. Our recipe is so general that scaling data and compute gives us predictable improvements. Unlocking one Solve accelerates the next Solve. The same ACT-2 model is learning to vacuum, organize toys, zip clothing, and turn pants inside out.

26,714 次观看 • 1 个月前

How can robots acquire fine-grained manipulation skills? Introducing ACT: Action Chunking with Transformers 🤖 Key idea: Imitation, but predict actions in chunks instead of one at a time. Here are results with only ~15min of demonstrations, running on low-cost arms:

How can robots acquire fine-grained manipulation skills? Introducing ACT: Action Chunking with Transformers 🤖 Key idea: Imitation, but predict actions in chunks instead of one at a time. Here are results with only ~15min of demonstrations, running on low-cost arms:

247,710 次观看 • 3 年前

Led by Google DeepMind, we present ALOHA 2 🤙: An Enhanced Low-Cost Hardware for Bimanual Teleoperation. ALOHA 2 🤙 significantly improves the durability of the original ALOHA 🏖️, enabling fleet-scale data collection on more complex tasks. As usual, everything is open-sourced!

Led by Google DeepMind, we present ALOHA 2 🤙: An Enhanced Low-Cost Hardware for Bimanual Teleoperation. ALOHA 2 🤙 significantly improves the durability of the original ALOHA 🏖️, enabling fleet-scale data collection on more complex tasks. As usual, everything is open-sourced!

144,185 次观看 • 2 年前

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