Grasp Transfer based on Self-Aligning Implicit Representations of Local
  Surfaces
Ahmet Tekden Marc Peter Deisenroth Yasemin Bekiroglu
Objects we interact with and manipulate often share similar parts, such as
handles, that allow us to transfer our actions flexibly due to their shared
functionality. 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. Existing approaches for
solving this problem are typically restricted to a specific object category or
a parametric shape. Our approach, however, can transfer grasps associated with
implicit models of local surfaces shared across object categories.
Specifically, we employ a single expert grasp demonstration to learn an
implicit local surface representation model from a small dataset of object
meshes. At inference time, this model is used to transfer grasps to novel
objects by identifying the most geometrically similar surfaces to the one on
which the expert grasp is demonstrated. Our model is trained entirely in
simulation and is evaluated on simulated and real-world objects that are not
seen during training. Evaluations indicate that grasp transfer to unseen object
categories using this approach can be successfully performed both in simulation
and real-world experiments. The simulation results also show that the proposed
approach leads to better spatial precision and grasp accuracy compared to a
baseline approach.