RI-Small: An HRI Approach to Robot Learning by Demonstration

Georgia Tech Research Corporation, Atlanta GA

Investigators

Abstract

For years, the robotics community has sought to enable robots to efficiently learn new skills from a human trainer. While motivated by the idea of robots that are easy to teach, there's been a lack of focus on several aspects essential to this goal. This project will advance the state of the art in robot Learning by Demonstration (LbD) by focusing the issues of learning from everyday human partners. LbD work has usually been evaluated with expert humans, typically the system designer. We will develop implementations and experiments on a humanoid social robot to address the following research questions: 1. In continuous embodied experience, how can a robot use cues from the environment and the person to segment its own learning examples? 2. What social cues does the human use to indicate salient aspects of the environment? What expressive mechanisms can the robot use to communicate relevant aspects of the learning state to the human partner? 3. Imitation learning is broader than the typical ?human-demo robot-repeat? LbD interaction. How can the robot use the human as an information source beyond generalizing a way to repeat motion trajectories? For example, it may be biased to interact with objects it?s seen the human interact with.