Lucy Shi @lucy_x_shi
Student researcher at Stanford. Working on robot learning and multimodal learning. Interested in robots, rockets, and humans. lucys0.github.io Palo Alto, CA Joined October 2021-
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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)
As impressive as always, great work @tonyzzhao!! Seeing Tony’s dexterous manipulation policies has changed my mind about what data can solve in the past year. Now a question that keeps me up at night is what data cannot or should not solve.
As impressive as always, great work @tonyzzhao!! Seeing Tony’s dexterous manipulation policies has changed my mind about what data can solve in the past year. Now a question that keeps me up at night is what data cannot or should not solve.
Yelling at robots is not very nice, because usually they can't understand what you're saying. But now thanks to @lucy_x_shi et al., they can! In YAY Robot, we study how language corrections (which can be spoken too, not yelled) allow robots to get better. A thread 👇
Imitation learning often involves significant human effort to collect a large dataset for robust policy learning. How can we train robust policies in low-data regimes? Our imitation learning framework PRIME scaffolds manipulation tasks with behavior primitives, breaking down…
Robots often make mistakes in long-horizon tasks, which require a lot of data to fix. Our robot can: - incorporate verbal corrections on-the-fly, AND - use those to improve the policy over time No further teleop needed! Paper & Code: yay-robot.github.io
Robots often make mistakes in long-horizon tasks, which require a lot of data to fix. Our robot can: - incorporate verbal corrections on-the-fly, AND - use those to improve the policy over time No further teleop needed! Paper & Code: yay-robot.github.io
In all seriousness though, being able to "program" *and* "debug" your robot in natural language will be tremendously useful when the job of teaching robots new skills is no longer done by machine learning experts in labs but end users in homes! Great job Lucy!! :)
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
We're diving into the world of humanoid robotics 🤖! We were curious how to train humanoid robots and ended up making HumanoidBench, a simulated benchmark for humanoid robots! Give it a try and let us know what you think!
We're diving into the world of humanoid robotics 🤖! We were curious how to train humanoid robots and ended up making HumanoidBench, a simulated benchmark for humanoid robots! Give it a try and let us know what you think!
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
Can we use wearable devices to collect robot data without actual robots? Yes! With a pair of gloves🧤! Introducing DexCap, a portable hand motion capture system that collects 3D data (point cloud + finger motion) for training robots with dexterous hands Everything open-sourced
🚨 Big news 🚨 Together with a set of amazing folks we decided to start a company that tackles one of the hardest and most impactful problems - Physical Intelligence In fact, we even named our company after that: physicalintelligence.company or Pi (π) for short 🧵
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"the only useful robot is a reliable robot"
Forbes asked me about Tesla’s claim they’ll sell humanoid robots to customers next year. My take: The only useful robot is a reliable robot. For humanoids to be ready for real work we can’t just be impressed by them, we need to be able to count on them. forbes.com/sites/robertha…
The power of street art - a thread 🧵 1. Patrick Commecy
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/
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!
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…
Amazing cross-platform system for multiple end-effectors. Nice work @xuxin_cheng and super fast move!
 🤖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…
The world of hardware is accelerating fast. Attention to the whole system, not just software/AI, will be necessary for real embodied AI.
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…
Excited to announce Tau Robotics (@taurobots). We are building a general AI for robots. We start by building millions of robot arms that learn in the real world. In the video, two robot arms are fully autonomous and controlled by a single neural network conditioned on different…
This was an awesome project - we teach models to follow constitutional principles with self-supervision (no labels). We also show that a weak model can generate principles for a stronger one, which self-aligns (SUPERALIGNENT!) and can beat the instruction-tuned (RLHF-ed) model!
Constitutional AI showed LMs can learn to follow constitutions by labeling their own outputs. But why can't we just tell a base model the principles of desired behavior and rely on it to act appropriately? Introducing SAMI: Self-Supervised Alignment with Mutual Information!
Some personal updates: I joined OpenAI a few months ago, working on all things robustness/safety/privacy. Also, we are working to publish more of our safety work. See my first project here below, where we make initial progress on prompt injections and other attacks!
Introducing the Instruction Hierarchy, our latest safety research to advance robustness for prompt injections and other ways of tricking LLMs into executing unsafe actions. More details: arxiv.org/abs/2404.13208
3D Gaussian is great, but how can you interact with it 🌹👋? Introducing #PhysDreamer: Create your own realistic interactive 3D assets from only static images! Discover how we do this below👇 🧵1/: Website: physdreamer.github.io
Training neural networks on 3D rotations feels at times like alchemy. Which rotation representation should we use? How critical is the choice of loss? Our recent preprint looks at these questions with clear visuals and simple math. Check out 👇 arxiv.org/abs/2404.11735 1/ 🧵
Thrilled to receive the @Google Research Scholar Award! Thanks to @GoogleAI, we will push the frontier of safe and scalable robot behavior alignment from human feedback. 🤖🤝👩 research.google/programs-and-e…
Curious about socially-intelligent AI? Check out our paper on underlying technical challenges, open questions, and opportunities to advance social intelligence in AI agents: Work w/ @lpmorency, @pliang279 📰Paper: arxiv.org/abs/2404.11023 💻Repo: github.com/l-mathur/socia… 🧵1/9
Shoutout to the folks at Rerun who built a visualizer for our DROID dataset -- looks very cool! Allows you to visualize the point cloud from our multi-view stereo cams as well! And should work for any new dataset collected on the DROID robot platform! Thanks @rerundotio :)
A Rerun Viewer for the DROID Dataset! DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset is a robot manipulation dataset by @SashaKhazatsky et al. with 76k demonstration trajectories or 350h of interaction data, collected across 564 scenes and 86 tasks.
I'm thrilled to join Princeton's faculty as an assistant professor in the ECE department starting Fall 2025 🐯 Stay tuned for the launch of my lab. We will develop generally helpful robots that learn and plan 🤖
We should send rockets not at each other, but rather to the stars
We have a new preprint out - your language model is not a reward, it’s a Q function! 1. The likelihood of the preferred answer must go down - it’s a policy divergence 2. MCTS guided decoding on language is equivalent to likelihood search on DPO 3. DPO learns credit assignment
Quick personal update-- Excited to share that I’ll be joining @Berkeley_EECS to start a PhD this autumn. Looking forward to start working w/ @JitendraMalikCV & @pabbeel! 🤖
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