Grasp Transfer based on Self-Aligning Implicit Representations of Local Surfaces
Journal article, 2023

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.

Adaptation models

Perception for Grasping and Manipulation

Deep Learning in Grasping and Manipulation

Training

Grasping

Shape

Codes

Grasping

Surface reconstruction

Object recognition

Author

Ahmet Ercan Tekden

Chalmers, Electrical Engineering, Systems and control

Marc Peter Deisenroth

University College London (UCL)

Yasemin Bekiroglu

Chalmers, Electrical Engineering, Systems and control

IEEE Robotics and Automation Letters

23773766 (eISSN)

Vol. 8 10 6315-6322

Subject Categories

Robotics

Computer Science

Computer Vision and Robotics (Autonomous Systems)

DOI

10.1109/LRA.2023.3306272

More information

Latest update

5/21/2024