Semantic grasping: planning task-specific stable robotic grasps

We present an example-based planning framework to generate semantic grasps , stable grasps that are functionally suitable for specific object manipulation tasks. We propose to use partial object geometry, tactile contacts, and hand kinematic data as proxies to encode task-related constraints, which...

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Bibliographic Details
Published in:Autonomous robots 2014-10, Vol.37 (3), p.301-316
Main Authors: Dang, Hao, Allen, Peter K.
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computer Imaging
Control
End effectors
Engineering
Grasping
Grasping (robotics)
Kinematics
Mechatronics
Pattern Recognition and Graphics
Planning
Robotics
Robotics and Automation
Robots
Searching
Semantics
Stability
Tactile
Tactile sensors (robotics)
Tasks
Vision
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Summary:We present an example-based planning framework to generate semantic grasps , stable grasps that are functionally suitable for specific object manipulation tasks. We propose to use partial object geometry, tactile contacts, and hand kinematic data as proxies to encode task-related constraints, which we call semantic constraints . We introduce a semantic affordance map , which relates local geometry to a set of predefined semantic grasps that are appropriate to different tasks. Using this map, the pose of a robot hand with respect to the object can be estimated so that the hand is adjusted to achieve the ideal approach direction required by a particular task. A grasp planner is then used to search along this approach direction and generate a set of final grasps which have appropriate stability, tactile contacts, and hand kinematics. We show experiments planning semantic grasps on everyday objects and applying these grasps with a physical robot.
ISSN:0929-5593
1573-7527
DOI:10.1007/s10514-014-9391-2