Guided Task Transfer in Interactive Robots

Ashok Goel, Andrea Thomaz, Henrik Christensen
Tesca Fitzgerald, Priyam Parashar

As robots become more commonplace, they will need to address a wide variety of problems. Since a robot cannot be programmed to complete every task, it is necessary for robots to learn new tasks by interacting with a human teacher. Current methods require that the robot receive many demonstrations of a task, or they are limited to completing tasks which are nearly identical to previous demonstrations. We are developing a cognitive system based on case-based analogical learning that may enable a robot to collaborate with a human teacher to transfer task knowledge to a range of target problems.

Ashok Goel
Ashok Goel, Keith McGreggor, Spencer Rugaber
Tesca Fitzgerald, David Joyner, Rochelle Lobo, Bryan Wiltgen, Gongbo Zhang

The Design & Intelligence Laboratory conducts research into human-centered artificial intelligence and computational cognitive science, with a focus on computational creativity. Current projects explore analogical reasoning in biologically inspired design, visual reasoning on intelligence tests, meta-reasoning in game-playing software agents, and learning about ecological and biological systems in science education.