A number of long-term goals in robotics – for example, using robots in household settings – will require robots that can interact with humans in an intuitive way. In this project, we explore how robots can learn to correlate natural language with the physical world being sensed and manipulated, an area of research that falls under grounded language acquisition. In particular, we are exploring how robots can learn from a small number of trials in which humans provide verbal descriptions of an action being demonstrated.

This project is affiliated with the Robotics and State Estimation Lab.

Publications

Following Directions Using Statistical Machine Translation
Cynthia Matuszek, Dieter Fox and Karl Koscher
ACM/IEEE International Conference on Human-Robot Interaction, 2010. Full Paper (PDF)