Knowledge-based control of grasping in robot hands using heuristics from human motor skills

Journal Article · Wed Dec 01 00:00:00 EST 1993 · IEEE Transactions on Robotics and Automation (Institute of Electrical and Electronics Engineers); (United States)

DOI:https://doi.org/10.1109/70.265915· OSTI ID:5040254

Bekey, G A ^[1]; Liu, H ^[2]; Tomovic, R ^[3]; Karplus, W J ^[4]

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1. Univ. of Southern California, Los Angeles, CA (United States). Computer Science Dept.
2. Telecom Research Labs., Clayton, Victoria (Australia). Artificial Intelligence Systems Section
3. Univ. of Belgrade (Yugoslavia). Dept. of Electrical Engineering
4. Univ. of California, Los Angeles, CA (United States). Computer Science Dept.

The development of a grasp planner for multifingered robot hands is described. The planner is knowledge-based, selecting grasp postures by reasoning from symbolic information on target object geometry and the nature of the task. The ability of the planner to utilize task information is based on an attempt to mimic human grasping behavior. Several task attributes and a set of heuristics derived from observation of human motor skills are included in the system. The paper gives several examples of the reasoning of the system in selecting the appropriate grasp mode for spherical and cylindrical objects for different tasks.

• OSTI ID: 5040254
• Journal Information: IEEE Transactions on Robotics and Automation (Institute of Electrical and Electronics Engineers); (United States), Vol. 9:6; ISSN 1042-296X
• Country of Publication: United States
• Language: English

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