4 research outputs found
Grasp Transfer based on Self-Aligning Implicit Representations of Local Surfaces
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.Comment: Accepted by IEEE RAL. 8 pages, 6 figures, 3 table
Grasp Transfer Based on Self-Aligning Implicit Representations of Local Surfaces
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
Affordance Transfer based on Self-Aligning Implicit Representations of Local Surfaces
Objects we interact with and manipulate often share similar parts, e.g. handles, that allow us to transfer our actions flexibly due to their shared functionality. This corresponds to affordances, i.e. set of action possibilities offered by the environment [1]. In this work, we propose to learn affordances associated with implicit models of local shapes shared across object categories. Our approach takes an expert grasp demon- stration on a given object, extracts the local geometry, and uses it as an anchor to align corresponding parts of objects from the same category. We show that the proposed implicit representation method can align objects within the same category under random pose perturbation. In addition, our general approach can align the local geometry to find grasp poses similar to the one demonstrated in the reference local shape. Finally, we show that we can identify the shared local geometry on novel objects from a different object category for affordance transfer