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Published on: April 21, 2023
Planning grasps for object manipulation: integrating internal preferences and external constraints.
Oliver Herbort1, Martin V Butz
1Department of Psychology, University of Würzburg, Röntgenring 11, 97070, Würzburg, Germany, oliver.herbort@psychologie.uni-wuerzburg.de.
Human grasp selection involves matching preferences with object shape. Planning time increases with grasp options, but unconstrained choices suggest a preference-based process, indicating a common criterion for grasp selection.
Area of Science:
- Cognitive Psychology
- Robotics
- Human-Computer Interaction
Background:
- Grasp selection integrates human preferences and object geometry.
- Two hypotheses explain grasp planning: simulation and preference-based selection.
- Understanding grasp planning is crucial for developing intuitive robotic manipulation.
Purpose of the Study:
- To investigate the cognitive processes underlying human grasp selection.
- To differentiate between the simulation and preference hypotheses of grasp planning.
- To determine how the number of grasp options influences planning time and final grasp choice.
Main Methods:
- Participants were tasked with grasping knobs offering two, four, or unconstrained grasp options.
- Planning time and grasp choices were recorded for each condition.
- Behavioral data were analyzed to compare planning efficiency and selection strategies.
Main Results:
- Planning time increased with the number of available grasp options (two vs. four), supporting the simulation hypothesis.
- Unconstrained grasp options resulted in planning times similar to the two-option condition, suggesting a different selection mechanism.
- Grasp choices remained consistent across conditions, irrespective of object shape, indicating a dominant selection criterion.
Conclusions:
- Grasp planning involves a complex interplay between simulation and preference-based mechanisms.
- The cognitive system may employ different strategies for grasp selection depending on the number of available options.
- A common, likely preference-driven, criterion guides final grasp selection, ensuring task success across varying constraints.
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