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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!

Tony Zhao

114,499 subscribers

144,078 просмотров • 2 лет назад •via X (Twitter)

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Комментарии: 10

Фото профиля Tony Z. Zhao

Tony Z. Zhao2 лет назад

Before diving into the hardware, we also release a *proper* ALOHA sim model with SysID, thanks to @kevin_zakka @the_real_btaba @ayzwah. Even if you don’t have the hardware, there is now a way to perform complex tasks with ALOHA in Mujoco!

Фото профиля Tony Z. Zhao

Tony Z. Zhao2 лет назад

We start by improving the grippers: to make them grasp better and more robust. We use a low-friction rail design that transmits 2x more force to the gripper tips. We also change the grip tape layout to improve grasping of small objects. Led by @SpencerGoodric6 and Thinh Nguyen

Фото профиля Tony Z. Zhao

Tony Z. Zhao2 лет назад

We use the same rail design on the leader side. To further improve ergonomics, we replace the original servo with a lower gear ratio one that is easier to backdrive. This results in a 10x reduction in friction that the operator needs to overcome when opening grippers!

Фото профиля Tony Z. Zhao

Tony Z. Zhao2 лет назад

Next, we improve the gravity compensation of the leader arm. With a constant-force retractor and a spring-pulley system, the arm can "float" in most places. It is also much more durable than the original rubberbands!

Фото профиля Tony Z. Zhao

Tony Z. Zhao2 лет назад

Last but not least: we simplify the frame surrounding the workcell while maintaining the rigidity of the camera mounting points. This opens up the space for both human-robot collaborators and props for the robot to interact with.

Фото профиля Tony Z. Zhao

Tony Z. Zhao2 лет назад

To learn more, please visit our website: Paper: Tutorial: Designs: Sim:

Фото профиля Tony Z. Zhao

Tony Z. Zhao2 лет назад

Thanks to the core ALOHA 2 Team: @RandomRobotics @chelseabfinn @peteflorence @SpencerGoodric6 Thinh Nguyen @JonathanTompson @ayzwah @tonyzzhao and those who helped with hardware, software, data, simulation, and user studies: Jorge Aldaco, Robert Baruch, Jeff Bingham, Sanky Chan, Kenneth Draper, @debidatta, Wayne Gramlich, Torr Hage, @AlexHerzog00, Jonathan Hoech, Ian Storz, @the_real_btaba, @leilatakayama, Ted Wahrburg, Sichun Xu, Sergey Yaroshenko, and @kevin_zakka

Фото профиля Masato Kobayashi @るっと🐺

Masato Kobayashi @るっと🐺2 лет назад

@GoogleDeepMind @tonyzzhao This is an incredibly fantastic achievement! I'm excited! Please look forward to our report on interesting research about imitation learning as well! This is part of that research.

Фото профиля Brett Adcock

Brett Adcock2 лет назад

@GoogleDeepMind good work Tony / team

Фото профиля Keerthana Gopalakrishnan

Keerthana Gopalakrishnan2 лет назад

@GoogleDeepMind lmfao @peteflorence and @andyzeng_ 🤣

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ALOHA is an open-source hardware System for bimanual teleoperation. With a $20k budget, it is capable of teleoperating precise dexterity tasks like these ones [source, read more:

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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)

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151,582 просмотров • 1 год назад

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Jim Fan

670,787 просмотров • 1 месяц назад

I was really impressed by the UMI gripper (Cheng Chi et al.), but a key limitation is that **force-related data wasn’t captured**: humans feel haptic feedback through the mechanical springs, but the robot couldn’t leverage that info, limiting the data’s value for fine-grained manipulation tasks. Led by my amazing students Yolanda Zhu and Binghao Huang, we designed a **portable visuo-tactile gripper** by integrating our dense, flexible tactile arrays with the UMI gripper to enable large-scale in-the-wild data collection. 🔗 We demonstrate **cross-modal representation learning** and **downstream policy learning** on tasks requiring in-hand state estimation (e.g., test tube reorientation) and fine-grained force sensing (e.g., pipette fluid transfer). Key takeaways: - Our flexible tactile arrays store the rich haptic information humans perceive as dense tactile signals. - Portability and robustness are key for in-the-wild data collection; our portable gripper is compact, lightweight, and durable. - Touch provides precise, robust measurements of in-hand object pose, invariant to lighting and viewpoint. - Cross-modal pretraining on large-scale in-the-wild data significantly improves policy robustness and sample efficiency (as shown many times before — and verified again here!). Also check out our previous investigations of dense, flexible tactile grids for understanding human-robot-environment interactions: - Dense tactile glove (Nature ’19): - 3D-ViTac (CoRL ’24):

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13,188 просмотров • 1 год назад

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Open-source robot arm meets hand tracking [📍GitHub below] It is designed with an industrial mindset but built as a 3D-printed desktop system. PAROL6 paired with a LEAP Motion controller is a nice example of how accessible robot teleoperation has become. • Hand motion is streamed to the robot at 100 Hz via UDP • A pneumatic gripper is controlled by simple fist open and close gestures • The entire robot stack is open source, from mechanics to control software Combine that with low-latency hand tracking and you get a very practical platform for learning manipulation, teleoperation, and human-robot interfaces. This kind of setup is great for experimentation, teleop, data collection, and teaching robots by demonstration All without proprietary hardware or locked software. Credit to SourceRobotics 📍Code: —— Weekly robotics and AI insights. Subscribe free:

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Xiao Ma

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Steve Barbour

27,049 просмотров • 2 лет назад

An interactive world model developed by NVIDIA in collaboration with academic partners. - DreamDojo turns egocentric human video data into physical intelligence. - Human data is more scalable than robotics data but lacks action labels. - To solve this, a dedicated action model extracts latent actions by identifying physics and motion deltas between frames. Training - A massive 44k hours of video data are used for pre-training. - Post-training on small-scale robot datasets maps human physics to specific robot embodiments. - An additional distillation stage converts the model into an autoregressive, few-step diffusion model, enabling real-time, action-controllable simulation. Primary Use Cases - Live Teleoperation: Controlling a robot inside a world simulation in real-time. - Model-based Planning: Previewing and curating the best actions for improved success. - Policy Evaluation: Testing robot policies in realistic, out-of-distribution scenarios. Everything that's open-sourced: weights, code, post-training dataset, eval set, and details to reproduce.

An interactive world model developed by NVIDIA in collaboration with academic partners. - DreamDojo turns egocentric human video data into physical intelligence. - Human data is more scalable than robotics data but lacks action labels. - To solve this, a dedicated action model extracts latent actions by identifying physics and motion deltas between frames. Training - A massive 44k hours of video data are used for pre-training. - Post-training on small-scale robot datasets maps human physics to specific robot embodiments. - An additional distillation stage converts the model into an autoregressive, few-step diffusion model, enabling real-time, action-controllable simulation. Primary Use Cases - Live Teleoperation: Controlling a robot inside a world simulation in real-time. - Model-based Planning: Previewing and curating the best actions for improved success. - Policy Evaluation: Testing robot policies in realistic, out-of-distribution scenarios. Everything that's open-sourced: weights, code, post-training dataset, eval set, and details to reproduce.

The Humanoid Hub

11,575 просмотров • 5 месяцев назад

Highlights of the OpenLedger journey: OpenLedger is the AI Blockchain unlocking liquidity for data, models, and agents, backed by Polychain, Balaji Srinivasan (ex-Coinbase CTO), and more. $14.7M+ in revenue from global enterprise projects and $91M+ in investment commitments secured. Worked with global enterprises like Walmart, Sony, Meta LLaMA, Viacom, and others. Built on Stanford’s data attribution research paper, enabling verifiable, fair rewards for contributors. 40+ projects, including ex-Google DeepMind teams and top DeFi builders, are building specialized AI models. 1M+ AI nodes, 25M+ transactions, and 20K+ models, real-world usage at scale.

Highlights of the OpenLedger journey: OpenLedger is the AI Blockchain unlocking liquidity for data, models, and agents, backed by Polychain, Balaji Srinivasan (ex-Coinbase CTO), and more. $14.7M+ in revenue from global enterprise projects and $91M+ in investment commitments secured. Worked with global enterprises like Walmart, Sony, Meta LLaMA, Viacom, and others. Built on Stanford’s data attribution research paper, enabling verifiable, fair rewards for contributors. 40+ projects, including ex-Google DeepMind teams and top DeFi builders, are building specialized AI models. 1M+ AI nodes, 25M+ transactions, and 20K+ models, real-world usage at scale.

OpenLedger

734,977 просмотров • 11 месяцев назад

Google has launched Med-Gemini, an advanced AI fine-tuned for medical Tasks. It significantly outperforms earlier models, including GPT-4, on most medical benchmarks. Achieves top scores, particularly on the MedQA-USMLE benchmark with a groundbreaking 91.1% accuracy.🚀 Demonstrates superior performance over GPT-4 by 44.5% on average across seven multimodal benchmarks. Excels in tasks such as medical summarization, generating doctor referrals, and simplifying medical documents. It is a preferred method over human expert analyses for complex text-based medical tasks. This marks a significant advancement in AI for healthcare, suggesting potential improvements in medical diagnostics and patient care. By choosing to harmonize with technology, we become more human, we become IRREPLACEABLE. Join the IRREPLACEABLE Academy and read the Book: #techforgood #medicine #ai

Google has launched Med-Gemini, an advanced AI fine-tuned for medical Tasks. It significantly outperforms earlier models, including GPT-4, on most medical benchmarks. Achieves top scores, particularly on the MedQA-USMLE benchmark with a groundbreaking 91.1% accuracy.🚀 Demonstrates superior performance over GPT-4 by 44.5% on average across seven multimodal benchmarks. Excels in tasks such as medical summarization, generating doctor referrals, and simplifying medical documents. It is a preferred method over human expert analyses for complex text-based medical tasks. This marks a significant advancement in AI for healthcare, suggesting potential improvements in medical diagnostics and patient care. By choosing to harmonize with technology, we become more human, we become IRREPLACEABLE. Join the IRREPLACEABLE Academy and read the Book: #techforgood #medicine #ai

Pascal Bornet

14,393 просмотров • 1 год назад