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This will redefine “remote” work: Fine-Grained Bimanual Manipulation with Low-Cost Hardware, from Tony Zhao at Stanford. Incredible precision.

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26,427 görüntüleme • 3 yıl önce •via X (Twitter)

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How can robots acquire fine-grained manipulation skills? Introducing ACT: Action Chunking with Transformers 🤖 Key idea: Imitation, but predict actions in chunks instead of one at a time. Here are results with only ~15min of demonstrations, running on low-cost arms:
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How can robots acquire fine-grained manipulation skills? Introducing ACT: Action Chunking with Transformers 🤖 Key idea: Imitation, but predict actions in chunks instead of one at a time. Here are results with only ~15min of demonstrations, running on low-cost arms:

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What are the essentials for today's robotic manipulation🤖? Our answer: 🦾: "Position and Force information/control" 💡: "A Low-cost bimanual robotic physical hardware considering diverse motor control modes (ALPHA-α)" Project Page:
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What are the essentials for today's robotic manipulation🤖? Our answer: 🦾: "Position and Force information/control" 💡: "A Low-cost bimanual robotic physical hardware considering diverse motor control modes (ALPHA-α)" Project Page:

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Enough with LLMs - exciting things are happening in the world of atoms. This is Stanford ALOHA, a low-cost and agile robot platform. The whole system is open-source (!!): hardware design, CAD models for 3D printing, simulator, and training code. Time to graft a physical arm onto GPTs 🦾 Led by my friend Tony Zhao at Stanford, and advised by Chelsea Finn Sergey Levine Vikash Kumar. Project page: If you don't want to 3D print your own components, you can buy the setup at
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Enough with LLMs - exciting things are happening in the world of atoms. This is Stanford ALOHA, a low-cost and agile robot platform. The whole system is open-source (!!): hardware design, CAD models for 3D printing, simulator, and training code. Time to graft a physical arm onto GPTs 🦾 Led by my friend Tony Zhao at Stanford, and advised by Chelsea Finn Sergey Levine Vikash Kumar. Project page: If you don't want to 3D print your own components, you can buy the setup at

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Wow. More great learning based bipedal control from Disney research. Doing this on low cost hardware is even more impressive.
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Researchers at Stanford University & University of Michigan, using our technology, developed a BCI enabling finger-based quadcopter control—using thought alone. Key highlights: -4 Degrees of Freedom: Decodes fine finger movements, doubling prior systems’ functionality. -Exceptional Precision: 76 targets/min with 100% accuracy. -Broader Impact: Paves the way for gaming, fine motor skill restoration & remote work. Congrats to the exceptional team! 🔗Read more in Nature Medicine:

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Can we collect robot data without any robots? Introducing Universal Manipulation Interface (UMI) An open-source $400 system from Stanford University designed to democratize robot data collection 0 teleop -> autonomously wash dishes (precise), toss (dynamic), and fold clothes (bimanual)

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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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Can we learn whole-body mobile manipulation directly from human demonstrations? Introducing Whole-Body Mobile Manipulation Interface (HoMMI) Egocentric + UMI, 0 teleop -> bimanual & whole-body manipulation, long-horizon navigation, active perception
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Existing 3D human manipulation datasets are valuable, but are limited in scale and diversity. At #CVPR2025, we will introduce GigaHands👐 which, to our knowledge, is the most extensive 3D bimanual manipulation, interaction, and gesture dataset.🧵👇(1/9)
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Existing 3D human manipulation datasets are valuable, but are limited in scale and diversity. At #CVPR2025, we will introduce GigaHands👐 which, to our knowledge, is the most extensive 3D bimanual manipulation, interaction, and gesture dataset.🧵👇(1/9)

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