Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation

Task demonstrations are an intuitive way of speaking advanced responsibilities to a robot. A current study on attempts to create a robotic process that can understand select-and-spot responsibilities for unseen objects in a information-successful method.

Robotic grippers. Image credit rating: Ars Electronica through Flickr, CC BY-NC-ND two.

Scientists suggest a novel technique, called Neural Descriptor Fields, to encode dense correspondence throughout object occasions. The coordinate frames are related with a local geometric framework making use of a rigid established of question points represented as an SE(3) pose. Dense descriptors that each generalize throughout occasions and SE(3) configurations are designed. That enables implementing the method to novel objects in each novel rotations and translations, in which 2d dense descriptors are inadequate.

Neural Descriptor Fields permits each select and spot of unseen object occasions in out-of-distribution configurations with a achievements amount previously mentioned 85% whilst making use of only ten expert demonstrations.

We present Neural Descriptor Fields (NDFs), an object illustration that encodes each points and relative poses in between an object and a concentrate on (this kind of as a robot gripper or a rack made use of for hanging) through classification-amount descriptors. We make use of this illustration for object manipulation, in which provided a activity demonstration, we want to repeat the exact activity on a new object occasion from the exact classification. We suggest to reach this objective by seeking (through optimization) for the pose whose descriptor matches that noticed in the demonstration. NDFs are conveniently qualified in a self-supervised vogue through a 3D vehicle-encoding activity that does not count on expert-labeled keypoints. Further more, NDFs are SE(3)-equivariant, guaranteeing performance that generalizes throughout all probable 3D object translations and rotations. We exhibit understanding of manipulation responsibilities from few (five-10) demonstrations each in simulation and on a actual robot. Our performance generalizes throughout each object occasions and 6-DoF object poses, and significantly outperforms a current baseline that depends on 2d descriptors. Project site: this https URL.

Study paper: Simeonov, A., “Neural Descriptor Fields: SE(3)-Equivariant Item Representations for Manipulation”, 2021. Backlink: muscles/2112.05124