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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

88,850 subscribers

144,015 Aufrufe • vor 2 Jahren •via X (Twitter)

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Profilbild von Tony Z. Zhao
Tony Z. Zhaovor 2 Jahren

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!

Profilbild von Tony Z. Zhao
Tony Z. Zhaovor 2 Jahren

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

Profilbild von Tony Z. Zhao
Tony Z. Zhaovor 2 Jahren

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!

Profilbild von Tony Z. Zhao
Tony Z. Zhaovor 2 Jahren

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!

Profilbild von Tony Z. Zhao
Tony Z. Zhaovor 2 Jahren

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.

Profilbild von Tony Z. Zhao
Tony Z. Zhaovor 2 Jahren

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

Profilbild von Tony Z. Zhao
Tony Z. Zhaovor 2 Jahren

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

Profilbild von Masato Kobayashi @るっと🐺
Masato Kobayashi @るっと🐺vor 2 Jahren

@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.

Profilbild von Brett Adcock
Brett Adcockvor 2 Jahren

@GoogleDeepMind good work Tony / team

Profilbild von Keerthana Gopalakrishnan
Keerthana Gopalakrishnanvor 2 Jahren

@GoogleDeepMind lmfao @peteflorence and @andyzeng_ 🤣

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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

733,837 Aufrufe • vor 10 Monaten

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: 🧵
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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

465,968 Aufrufe • vor 1 Jahr