Investigating Learning from Demonstration in Imperfect and Real World Scenarios

Abstract

As the world's population is aging and there are growing shortages of caregivers, research into assistive robots is increasingly important. Due to differing needs and preferences, which may change over time, end-users will need to be able to communicate their preferences to a robot. Learning from Demonstration (LfD) is one method that enables non-expert users to program robots. While a powerful tool, prior research in LfD has made assumptions that break down in real-world scenarios. In this work, we investigate how to learn from suboptimal and heterogeneous demonstrators, how users react to failure with LfD, and the feasibility of LfD with a target population of older adults.