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This paper presents an approach to passively realize any specified object spatial compliance using the grasp of a robotic hand. The kinematically anthropomorphic hands considered have multiple 4-joint fingers making hard point contact with the held object, and the joints of each finger have selectable passive elastic behavior. It is shown that the space of passively realizable compliances is restricted by the kinematic structure of the anthropomorphic hand. To achieve an arbitrary compliant behavior, fingers must be able to adjust their orientation. Synthesis procedures for grasps having 3, 4, and 5 or more fingers are developed. These procedures identify the finger configurations and the individual finger joint compliances needed to passively achieve any specified spatial object compliance matrix in the 20-dimensional subspace of grasp-realizable behaviors.
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Improving Grasp Classification through Spatial Metrics Available from Sensors
We present a method for classifying the quality of near-contact grasps using spatial metrics that are recoverable from sensor data. Current methods often rely on calculating precise contact points, which are difficult to calculate in real life, or on tactile sensors or image data, which may be unavailable for some applications. Our method, in contrast, uses a mix of spatial metrics that do not depend on the fingers being in contact with the object, such as the object's approximate size and location. The grasp quality can be calculated {\em before} the fingers actually contact the object, enabling near-grasp quality prediction. Using a random forest classifier, the resulting system is able to predict grasp quality with 96\% accuracy using spatial metrics based on the locations of the robot palm, fingers and object. Furthermore, it can maintain an accuracy of 90\% when exposed to 10\% noise across all its inputs.
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- Award ID(s):
- 1911050
- PAR ID:
- 10275405
- Date Published:
- Journal Name:
- IEEE International Conference on Robotics and Automation
- ISSN:
- 1049-3492
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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