Robotic learning of haptic adjectives through physical interaction

To perform useful tasks in everyday human environments, robots must be able to both understand and communicate the sensations they experience during haptic interactions with objects. Toward this goal, we augmented the Willow Garage PR2 robot with a pair of SynTouch BioTac sensors to capture rich tac...

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Published inRobotics and autonomous systems Vol. 63; pp. 279 - 292
Main Authors Chu, Vivian, McMahon, Ian, Riano, Lorenzo, McDonald, Craig G., He, Qin, Martinez Perez-Tejada, Jorge, Arrigo, Michael, Darrell, Trevor, Kuchenbecker, Katherine J.
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.01.2015
Subjects
Online AccessGet full text
ISSN0921-8890
1872-793X
DOI10.1016/j.robot.2014.09.021

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Abstract To perform useful tasks in everyday human environments, robots must be able to both understand and communicate the sensations they experience during haptic interactions with objects. Toward this goal, we augmented the Willow Garage PR2 robot with a pair of SynTouch BioTac sensors to capture rich tactile signals during the execution of four exploratory procedures on 60 household objects. In a parallel experiment, human subjects blindly touched the same objects and selected binary haptic adjectives from a predetermined set of 25 labels. We developed several machine-learning algorithms to discover the meaning of each adjective from the robot’s sensory data. The most successful algorithms were those that intelligently combine static and dynamic components of the data recorded during all four exploratory procedures. The best of our approaches produced an average adjective classification F1 score of 0.77, a score higher than that of an average human subject. •We equipped a PR2 robot with a pair of BioTac tactile sensors.•Both the robot and human subjects blindly touched sixty diverse objects.•We calculated static and dynamic features from the haptic data felt by the robot.•A multi-kernel SVM was used to learn the meaning of twenty-five haptic adjectives.•The robot performed as well as the average human subject at labeling objects.
AbstractList To perform useful tasks in everyday human environments, robots must be able to both understand and communicate the sensations they experience during haptic interactions with objects. Toward this goal, we augmented the Willow Garage PR2 robot with a pair of SynTouch BioTac sensors to capture rich tactile signals during the execution of four exploratory procedures on 60 household objects. In a parallel experiment, human subjects blindly touched the same objects and selected binary haptic adjectives from a predetermined set of 25 labels. We developed several machine-learning algorithms to discover the meaning of each adjective from the robot's sensory data. The most successful algorithms were those that intelligently combine static and dynamic components of the data recorded during all four exploratory procedures. The best of our approaches produced an average adjective classification score of 0.77, a score higher than that of an average human subject.
To perform useful tasks in everyday human environments, robots must be able to both understand and communicate the sensations they experience during haptic interactions with objects. Toward this goal, we augmented the Willow Garage PR2 robot with a pair of SynTouch BioTac sensors to capture rich tactile signals during the execution of four exploratory procedures on 60 household objects. In a parallel experiment, human subjects blindly touched the same objects and selected binary haptic adjectives from a predetermined set of 25 labels. We developed several machine-learning algorithms to discover the meaning of each adjective from the robot’s sensory data. The most successful algorithms were those that intelligently combine static and dynamic components of the data recorded during all four exploratory procedures. The best of our approaches produced an average adjective classification F1 score of 0.77, a score higher than that of an average human subject. •We equipped a PR2 robot with a pair of BioTac tactile sensors.•Both the robot and human subjects blindly touched sixty diverse objects.•We calculated static and dynamic features from the haptic data felt by the robot.•A multi-kernel SVM was used to learn the meaning of twenty-five haptic adjectives.•The robot performed as well as the average human subject at labeling objects.
Author He, Qin
Chu, Vivian
McMahon, Ian
Arrigo, Michael
Riano, Lorenzo
McDonald, Craig G.
Martinez Perez-Tejada, Jorge
Darrell, Trevor
Kuchenbecker, Katherine J.
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Robotic perception
Machine learning
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Snippet To perform useful tasks in everyday human environments, robots must be able to both understand and communicate the sensations they experience during haptic...
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SubjectTerms Algorithms
Classification
Haptics
Human
Machine learning
Robotic perception
Robotics
Robots
Tactile sensing
Tasks
Time-series classification
Willow
Title Robotic learning of haptic adjectives through physical interaction
URI https://dx.doi.org/10.1016/j.robot.2014.09.021
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