Maneuvering Robot Companions via Human Facial Expressions In Human-Robot Collaborative Tasks
School Name
South Carolina Governor's School for Science & Mathematics
Grade Level
12th Grade
Presentation Topic
Computer Science
Presentation Type
Mentored
Abstract
To maneuver robot companions in human-robot collaboration, most of the previous research focused on how to use external equipment, such as teach pedants or joy sticks, to control robots. However, these approaches are not always intuitive and effective for human partners in particular the disabled group. In this study, human facial expressions are employed to teach a collaborative robot to act according to the change of human's emotions. The Radial Basis Function (RBF) network is used alongside the Local Binary Patterns (LBP) approach in order to extract the human facial expressions and train the robot in human-robot collaboration contexts. Five distinct facial expressions—happy, sad, angry, surprised, and neutral—were designed and 1000 sets of samples were collected for each in the training process. Based on the learned knowledge, the robot can recognize different kinds of human facial expressions in real-time and reply with a collaborative action for the human. In this research, over a series of tests, we also found that the tested networks are able to perform better when both the training and the post-testing were done using a singular, solid color background. In addition, objects such as thick glasses or headphones can hinder the robot's ability to understand the facial expressions.
Recommended Citation
Rushton, Macon, "Maneuvering Robot Companions via Human Facial Expressions In Human-Robot Collaborative Tasks" (2020). South Carolina Junior Academy of Science. 279.
https://scholarexchange.furman.edu/scjas/2020/all/279
Location
Furman Hall 109
Start Date
3-28-2020 11:15 AM
Presentation Format
Oral Only
Group Project
No
Maneuvering Robot Companions via Human Facial Expressions In Human-Robot Collaborative Tasks
Furman Hall 109
To maneuver robot companions in human-robot collaboration, most of the previous research focused on how to use external equipment, such as teach pedants or joy sticks, to control robots. However, these approaches are not always intuitive and effective for human partners in particular the disabled group. In this study, human facial expressions are employed to teach a collaborative robot to act according to the change of human's emotions. The Radial Basis Function (RBF) network is used alongside the Local Binary Patterns (LBP) approach in order to extract the human facial expressions and train the robot in human-robot collaboration contexts. Five distinct facial expressions—happy, sad, angry, surprised, and neutral—were designed and 1000 sets of samples were collected for each in the training process. Based on the learned knowledge, the robot can recognize different kinds of human facial expressions in real-time and reply with a collaborative action for the human. In this research, over a series of tests, we also found that the tested networks are able to perform better when both the training and the post-testing were done using a singular, solid color background. In addition, objects such as thick glasses or headphones can hinder the robot's ability to understand the facial expressions.
