Model Learning Guided by Natural Language Instructions

The focus of my research is to develop methods to enable improved human-robot interaction through the use of a natural language interface for programming robotic tasks. We will focus on the problem of enabling human instruction giving in settings where the robot and human user do not already have a clear understanding of each other's vocabularies and skills. This calls for innovations on decision theoretic models of interaction and their coupling with language learning.
Building on recent work on interactive and adaptive language learning, and combining these works with other innovations on learning in ad hoc coordination settings, we will devise new models of adaptive language learning that will be specifically focussed on the needs of human-robot interaction. So, we will first begin by implementing an interactive system involving human users working together with a physical robot (the PR2, available in the EPSRC Robotarium Research facility) and use these experimental paradigms to collect and curate a corpus of data that includes information about the task, dialogue and environmental variables. We will then use this to learn new models and through the use of these models, explore new ways in which we can address problems such as learning a language in conjunction with skill adaptation.