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Title: Modeling Grasp Type Improves Learning-Based Grasp Planning
Authors:
;
Award ID(s):
1657596
Publication Date:
NSF-PAR ID:
10089725
Journal Name:
IEEE Robotics and Automation Letters
Volume:
4
Issue:
2
Page Range or eLocation-ID:
784 to 791
ISSN:
2377-3774
Sponsoring Org:
National Science Foundation
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  1. Soft isoperimetric truss robots have demonstrated an ability to grasp and manipulate objects using the members of their structure. The compliance of the members affords large contact areas with even force distribution, allowing for successful grasping even with imprecise open-loop control. In this work we present methods of analyzing and controlling isoperimetric truss robots in the context of grasping and manipulating objects. We use a direct stiffness model to characterize the structural properties of the robot and its interactions with external objects. With this approach we can estimate grasp forces and stiffnesses with limited computation compared to higher fidelity finite elements methods, which, given the many degrees-of-freedom of truss robots, are prohibitively expensive to run on-board. In conjunction with the structural model, we build upon a literature of differential kinematics for truss robots and apply it to the task of manipulating an object within the robot’s workspace.