Tony Z. Zhao @tonyzzhao
CS PhD student @Stanford. Aspiring full-stack roboticist. Prev Deepmind, Tesla, GoogleX, Berkeley. tonyzhaozh.github.io Stanford, CA Joined December 2018-
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Imitation learning works™ – but you need good data 🥹 How to get high-quality visuotactile demos from a bimanual robot with multifingered hands, and learn smooth policies? Check our new work “Learning Visuotactile Skills with Two Multifingered Hands”! 🙌 toruowo.github.io/hato/
The world of hardware is accelerating fast. Attention to the whole system, not just software/AI, will be necessary for real embodied AI.
The world of hardware is accelerating fast. Attention to the whole system, not just software/AI, will be necessary for real embodied AI.
Was incredibly fun coming to the @MKBHD studio to record a Waveform podcast episode with Marques and @DurvidImel — and also got to talk about my latest VR setup 🙃
Was incredibly fun coming to the @MKBHD studio to record a Waveform podcast episode with Marques and @DurvidImel — and also got to talk about my latest VR setup 🙃 https://t.co/3msqsmSsnG
Excited to share one of the last projects I worked on at Google -- We picked three of the most dextrous tasks we could think of: - tying shoelaces 👟 - replacing a robot finger 🤖 - hanging a shirt 👕 and tried to see if we could train a policy to do each. All of them worked!
Excited to share one of the last projects I worked on at Google -- We picked three of the most dextrous tasks we could think of: - tying shoelaces 👟 - replacing a robot finger 🤖 - hanging a shirt 👕 and tried to see if we could train a policy to do each. All of them worked!
This is probably the most surprising result I had in 2023!
This is probably the most surprising result I had in 2023!
For the past year we've been working on ALOHA Unleashed 🌋 @GoogleDeepMind - pushing the scale and dexterity of tasks on our ALOHA 2 fleet. Here is a thread with some of the coolest videos! The first task is hanging a shirt on a hanger (autonomous 1x)
We just cut a new offical release for BEHAVIOR-1K, with all 1,000 activities successfully sampled across 50 high-quality, interactive scenes, ready to be tackled by robotics AI and researchers! Visit our website and try it out today! behavior.stanford.edu
We just cut a new offical release for BEHAVIOR-1K, with all 1,000 activities successfully sampled across 50 high-quality, interactive scenes, ready to be tackled by robotics AI and researchers! Visit our website and try it out today! behavior.stanford.edu
UMI x ARX @YihuaiGao just got our in-the-wild cup policy working with ARX5 @ARX_Zhang ! We are still tuning the controller and latency matching for smoother tracking. Lot’s of potential in these low-cost lightweight arms!
End-to-end low-level skills with language corrections on the fly. Huge commitment from @lucy_x_shi to pull this off. Congratulations!
End-to-end low-level skills with language corrections on the fly. Huge commitment from @lucy_x_shi to pull this off. Congratulations!
Access to *diverse* training data is a major bottleneck in robot learning. We're releasing DROID, a large-scale in-the-wild manipulation dataset. 76k trajectories, 500+ scenes, multi-view stereo, language annotations etc Check it out & download today! 💻: droid-dataset.github.io
After two years, it is my pleasure to introduce “DROID: A Large-Scale In-the-Wild Robot Manipulation Dataset” DROID is the most diverse robotic interaction dataset ever released, including 385 hours of data collected across 564 diverse scenes in real-world households and offices
It is refreshing to see highly creative, open-source works like DexCap building on top of another highly creative, open-source work (LEAP hand by @kenny__shaw.) This is the best way forward for the community. Congratulations @chenwang_j !
It is refreshing to see highly creative, open-source works like DexCap building on top of another highly creative, open-source work (LEAP hand by @kenny__shaw.) This is the best way forward for the community. Congratulations @chenwang_j !
Unitree feels like… the elephant in the room?
Unitree feels like… the elephant in the room?
Check out UMI! 3 things I learned in this project: 1. Wrist-mount cameras can be sufficient for challenging manipulation tasks with the right hardware design. 2. Cross-embodiment policy is possible with the right policy interface. 3. BC can generalize if the data is right.
Check out UMI! 3 things I learned in this project: 1. Wrist-mount cameras can be sufficient for challenging manipulation tasks with the right hardware design. 2. Cross-embodiment policy is possible with the right policy interface. 3. BC can generalize if the data is right.
When I revisit the videos of the amazing work by @zipengfu, @tonyzzhao and @chelseabfinn, l realized how robust robotic behaviors can be recovered from demonstrations. Maybe imitation learning with high quality data is the right way to go?
When I revisit the videos of the amazing work by @zipengfu, @tonyzzhao and @chelseabfinn, l realized how robust robotic behaviors can be recovered from demonstrations. Maybe imitation learning with high quality data is the right way to go?
I wish we can come back to this tweet in a decade and be like "Hey here is when we finally cracked data collection". Low-cost, portable, hardware agnostic. I could not ask for more!
I wish we can come back to this tweet in a decade and be like "Hey here is when we finally cracked data collection". Low-cost, portable, hardware agnostic. I could not ask for more!
Impressive progress from the Stanford Robotics Center applying Gen AI (diffusion models) to training robots This work shows that low-cost, human-trained domestic robots are not too far in our future More details here: umi-gripper.github.io
Impressive progress from the Stanford Robotics Center applying Gen AI (diffusion models) to training robots This work shows that low-cost, human-trained domestic robots are not too far in our future More details here: umi-gripper.github.io
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Animals are intelligent agents that plan and act to accomplish complex goals. Can we try learning from them? We present EgoPet, a new ego centric video dataset of animals scraped from YouTube and TikTok.
data seems to be academic only, which is very sad.
This is the coolest dataset I've seen in a long time. Truly splendid
Animals are intelligent agents that plan and act to accomplish complex goals. Can we try learning from them? We present EgoPet, a new ego centric video dataset of animals scraped from YouTube and TikTok.
This line from the ALOHA paper confuses me. I thought the leader controls the follower and ACT only looks at current follower pos + RGB to predict the next follower positions. If I'm reading the code correctly, leader positions are not used. What are they trying to say here?
@BartronPolygon Let's say you're applying force to an immovable object, follower arms will be stuck at the object, while leader arms could still be moving, applying more force on the follower side. If you recorded follower positions this force wouldn't be captured.
Really pulling the tech founder starter pack huh
@GoddenThomas @ImperialDyson @thrishlab bro reinvented the wheel
One of the projects I worked on @ImperialDyson / @thrishlab is getting published! We designed a print-in-place compliant wheel that passively transforms when encountering obstacles. Video with more details: youtu.be/oZbZZlDDEnc?si… Early access paper: ieeexplore.ieee.org/abstract/docum…
To address the first challenge, we develop HATO ("dove" 🕊️ in Japanese), a low-cost Hands-Arms TeleOperation system using Meta Quest 2. Our system demonstrates precise motion control capabilities and human-like dexterity -- we even controlled our robot to play Hollow Knight!
@chichengcc Thank you Cheng! Indeed, look forward to seeing your next steps too 😄
Congrats Toru! I hope to see more bi-manual systems from the community Imitation learning works™
Imitation learning works™ – but you need good data 🥹 How to get high-quality visuotactile demos from a bimanual robot with multifingered hands, and learn smooth policies? Check our new work “Learning Visuotactile Skills with Two Multifingered Hands”! 🙌 toruowo.github.io/hato/
Check out our new bi-dex-hands. To me, it is quite a fun experience to move away from my comfort zone of sim-to-real, and learn about how to collect data. I was very bad at playing video games (teleop), but with this, even I can collect the steak training data in about 3 hours.
Imitation learning works™ – but you need good data 🥹 How to get high-quality visuotactile demos from a bimanual robot with multifingered hands, and learn smooth policies? Check our new work “Learning Visuotactile Skills with Two Multifingered Hands”! 🙌 toruowo.github.io/hato/
 🤖Introducing 📺𝗢𝗽𝗲𝗻-𝗧𝗲𝗹𝗲𝗩𝗶𝘀𝗶𝗼𝗻: a web-based teleoperation software!  🌐Open source, cross-platform (VisionPro & Quest) with real-time stereo vision feedback.  🕹️Easy-to-use hand, wrist, head pose streaming. Code: github.com/OpenTeleVision…
Imitation learning works™ – but you need good data 🥹 How to get high-quality visuotactile demos from a bimanual robot with multifingered hands, and learn smooth policies? Check our new work “Learning Visuotactile Skills with Two Multifingered Hands”! 🙌 toruowo.github.io/hato/
Super neat system! It seems that Chinese robotics startups have everything they need to quickly iterate on capable & low-cost hardware. Will US startups be able to compete? Chaining together dynamixals/off-the-shelf motors likely won’t cut it…
This is from a Chinese robot startup. Many of the tasks are the ones that I really wanted to show in my papers but failed to find a good enough robot.
Did anyone notice this? This is amazing! I think single-task robot manipulation is already completely solved through imitation learning. bilibili.com/video/BV1yJ4m1…
47 courses, 6 coops, and 5 years later - undergrad complete!
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