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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!

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Tony Zhao

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144,078 görüntüleme • 2 yıl önce •via X (Twitter)

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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!

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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

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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!

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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!

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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.

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

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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

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@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.

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@GoogleDeepMind good work Tony / team

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@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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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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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.
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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.

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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.
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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

734,883 görüntüleme • 11 ay önce

Excited to announce GR00T N1, the world’s first open foundation model for humanoid robots! We are on a mission to democratize Physical AI. The power of general robot brain, in the palm of your hand - with only 2B parameters, N1 learns from the most diverse physical action dataset ever compiled and punches above its weight: - Real humanoid teleoperation data. - Large-scale simulation data: we are open-sourcing 300K+ trajectories! - Neural trajectories: we apply SOTA video generation models to “hallucinate” new synthetic data that features accurate physics in pixels. Using Jensen’s words, “systematically infinite data”! - Latent actions: we develop novel algorithms to extract action tokens from in-the-wild human videos and neural generated videos. GR00T N1 is a single end-to-end neural net, from photons to actions: - Vision-Language Model (System 2) that interprets the physical world through vision and language instructions, enabling robots to reason about their environment and instructions, and plan the right actions. - Diffusion Transformer (System 1) that “renders” smooth and precise motor actions at 120 Hz, executing the latent plan made by System 2. We deploy N1 on GR1 robot, 1X Neo robot, and a large collection of simulation benchmarks. N1 achieves up to +30% boost in diverse manipulation tasks for household and industrial settings. While humanoid robots are the main focus of N1, our model also supports cross-embodiment. We finetune it to work on the $110 HuggingFace LeRobot SO100 robot arm! Open robot brain runs on open hardware. Sounds just right. Let’s solve robotics, together, one token at a time. Links to our Whitepaper, Github repo, HuggingFace model, and open dataset page in the thread: 🧵
2:21

Excited to announce GR00T N1, the world’s first open foundation model for humanoid robots! We are on a mission to democratize Physical AI. The power of general robot brain, in the palm of your hand - with only 2B parameters, N1 learns from the most diverse physical action dataset ever compiled and punches above its weight: - Real humanoid teleoperation data. - Large-scale simulation data: we are open-sourcing 300K+ trajectories! - Neural trajectories: we apply SOTA video generation models to “hallucinate” new synthetic data that features accurate physics in pixels. Using Jensen’s words, “systematically infinite data”! - Latent actions: we develop novel algorithms to extract action tokens from in-the-wild human videos and neural generated videos. GR00T N1 is a single end-to-end neural net, from photons to actions: - Vision-Language Model (System 2) that interprets the physical world through vision and language instructions, enabling robots to reason about their environment and instructions, and plan the right actions. - Diffusion Transformer (System 1) that “renders” smooth and precise motor actions at 120 Hz, executing the latent plan made by System 2. We deploy N1 on GR1 robot, 1X Neo robot, and a large collection of simulation benchmarks. N1 achieves up to +30% boost in diverse manipulation tasks for household and industrial settings. While humanoid robots are the main focus of N1, our model also supports cross-embodiment. We finetune it to work on the $110 HuggingFace LeRobot SO100 robot arm! Open robot brain runs on open hardware. Sounds just right. Let’s solve robotics, together, one token at a time. Links to our Whitepaper, Github repo, HuggingFace model, and open dataset page in the thread: 🧵

Jim Fan

466,333 görüntüleme • 1 yıl önce