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These days, it feels like a new robotic hand is developed every week. But how can we give them all vision-based general grasping ability? Meet AnyDexGrasp: it enables robust grasping across different robot hands—with just 40 training objects and 4–8 hours of trial and error!

Hao-Shu Fang

1,719 subscribers

25,409 次观看 • 1 年前 •via X (Twitter)

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Humans grasp objects with a purpose! Web2Grasp enables such functional grasping for dexterous robot hands via hand-object reconstruction from web images - without *any* robot teleop data collection 1/n

Humans grasp objects with a purpose! Web2Grasp enables such functional grasping for dexterous robot hands via hand-object reconstruction from web images - without any robot teleop data collection 1/n

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The full video of hand and glove co-design in action. For hand 🖐️ we try to make sure it's able to repeat what a human hand can do; hyperextension in the index finger is a great example here. Most robotic hands lack that, and it is very important for grasping objects securely. Let’s try to close the embodiment gap. For glove 🧤 we try to make sure it’s comfortable to wear, while also has same kinematics, contacts with the robot hand.

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Quanting Xie

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A robot hand grasp over 500 totally new objects without fail? Zero-shot, single-view & super reliable ⬇️ + Paper Grasping random objects is hard for robots, especially when shapes, weights, and materials vary. RobustDexGrasp solves this with a smart new way of seeing and controlling the hand, leading to near-perfect grasping, even in noisy or cluttered scenes. Thank you for sharing, Hui Zhang 🙏 Follow him!! What makes it special ✅ Grabs 500+ unseen objects with 94.6% success using only single-view input ✅ Learns local shapes, not full geometry, for better generalization ✅ Trained with just 35 objects in sim but works in the real world with hundreds more ✅ Adapts to noise, unexpected forces, and even plays chess with VLM planning It shows that smart sensing and adaptive control can take dexterous grasping to the next level. Project: Paper:

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Our Robotic Foundation Model, trained on data from real-world warehouse operations and simulated scenarios, enables our robots to identify objects, understand 3D spaces, and predict optimal grasping and placement.

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