“The students are gaining first-hand experience in a field that has potential implications for a range of applications, from assisting with elder care to working with children,” Barron said. “It’s exciting to see our students and faculty at the forefront of such a cutting-edge field of technology. The work these students are doing is impressive, not to mention fun.”
Nao comes from the company Aldebaran Robotics, a subsidiary of SoftBank. The students learn how to design interactions with the robot using a visual program interface tool. Students program Nao to dance, for example, or to express his “feelings” with the light around his eyes.
Students Kaley Chicoine and Lawrence John Flood, graduate teaching assistant Tyler Frederick and undergraduate learning assistant Jeffrey Lii made up the team whose work was showcased at the tailgate. Before that, Lii had worked with Frederick to set up the labs for the course.
“This provides the building blocks for students to think about how they can design a project toward the end of the semester that combines the different behaviors into interactions between the robot and the student teams,” Yen said.
The students’ projects range from having the robot study with them to Nao playing a role in a scene about quitting smoking. Nao can be pre-programmed to say and do certain things, but he can also be programmed to interact with humans and respond to questions.
Working with Nao also provides an opportunity for students to learn about technology and its history, artificial intelligence and what it means to be a “social robot.”
“There are multiple ways we try to use this as an anchoring point for various conversations,” Yen said. “One very natural conversation is to talk about the technology in the robot that has enabled the robot to do various things, and how robots have different levels of ‘intelligence.’ Students can learn about the history and a little bit about what the enabling technologies are that make these things happen.”
Yen said it is still a long way to the scenes in movies where the machines are at a much higher level of intelligence than humans.
“Our understanding of human intelligence is still growing. We’re trying to understand our brain more and more,” he said. “The more we understand how to build intelligent machines, the more it will help us understand human intelligence. And, the more we understand human intelligence, the more it will help us build intelligent machines.”