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Found 3 papers, 2 papers with code
Self-Supervised Correspondence in Visuomotor Policy Learning
In this paper we explore using self-supervised correspondence for improving the generalization performance and sample efficiency of visuomotor policy learning.
kPAM: KeyPoint Affordances for Category-Level Robotic Manipulation
However, representing an object with a parameterized transformation defined on a fixed template cannot capture large intra-category shape variation, and specifying a target pose at a category level can be physically infeasible or fail to accomplish the task -- e. g. knowing the pose and size of a coffee mug relative to some canonical mug is not sufficient to successfully hang it on a rack by its handle.
Robotics
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
In this work, we introduce DeepSDF, a learned continuous Signed Distance Function (SDF) representation of a class of shapes that enables high quality shape representation, interpolation and completion from partial and noisy 3D input data.
