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Robots that learn should generalize. Locomotion should show generalization across terrain; manipulation across object categories. Here is a video of “Pick Anything”, demonstrating human like grasps for 66 different objects, ranging in size and shape from air pods to dumbbells to pipe wrenches, using a *single* policy. The system... show more
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8 Kommentare

RL in sim and real?

66 objects from AirPods to dumbbells is the right kind of demo: breadth, not one hero task. From the data-collection side, my question is where the grasp diversity came from. Human demos, sim, or the policy itself? That ratio decides what data teams should actually pay for.

Zero teleoperation data and still human-like grasps on stuff as different as AirPods and pipe wrenches? That's a big deal. Curious what it does with weird geometry, any fun failure cases to share?

66 objects from airpods to dumbbells is a crazy range. the airpods are probably the harder one right? small and slippery is way worse than heavy

How many of the 66 were never seen in training? The dumbbell and the pipe wrench are a lot more convincing if they're new to the policy.

Objects like these were used in training (indeed lot more than these 66). If you want to learn human-like grasps you need to show examples of humans grasping various objects. But once you show "enough" of these, the magic of big data kicks in. Generalization across lighting, position, background clutter is automatic and doesn't need big data.

Got it, thanks. Did you hit a rough point where more human grasp videos stopped helping? I'm curious how big "enough" turned out to be.

Great experience
