Recognition by functional parts
Computer Vision and Image Understanding - Special issue of funtion-based vision
Interactive recognition and representation of functionality
Computer Vision and Image Understanding - Special issue of funtion-based vision
Generic object recognition using form and function
Generic object recognition using form and function
Mechanics of robotic manipulation
Mechanics of robotic manipulation
An Behavior-based Robotics
Identification of functional features through observations and interactions
Identification of functional features through observations and interactions
Learning Objects and Grasp Affordances through Autonomous Exploration
ICVS '09 Proceedings of the 7th International Conference on Computer Vision Systems: Computer Vision Systems
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This paper introduces a behavior-grounded approach to representing and learning the affordances of tools by a robot. The affordance representation is learned during a behavioral babbling stage in which the robot randomly chooses different exploratory behaviors, applies them to the tool, and observes their effects on environmental objects. As a result of this exploratory procedure, the tool representation is grounded in the behavioral and perceptual repertoire of the robot. Furthermore, the representation is autonomously testable and verifiable by the robot as it is expressed in concrete terms (i.e., behaviors) that are directly available to the robot's controller. The tool representation described here can also be used to solve tool-using tasks by dynamically sequencing the exploratory behaviors which were used to explore the tool based on their expected outcomes. The quality of the learned representation was tested on extension-of-reach tasks with rigid tools.