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Found 3 papers, 1 papers with code
A Unifying Variational Framework for Gaussian Process Motion Planning
To control how a robot moves, motion planning algorithms must compute paths in high-dimensional state spaces while accounting for physical constraints related to motors and joints, generating smooth and stable motions, avoiding obstacles, and preventing collisions.
Grasp Transfer based on Self-Aligning Implicit Representations of Local Surfaces
This work addresses the problem of transferring a grasp experience or a demonstration to a novel object that shares shape similarities with objects the robot has previously encountered.
Learning a generative model for robot control using visual feedback
Our method can handle noise in the observed state and noise in the controllers that we interact with.
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