A Unified Knowledge Representation System for Robot Learning and Dialogue

To allow wide-spread adoption of consumer robotics, robots must be able to adapt to their environment by learning new skills and communicating with humans. Each chapter explains a contribution to achieve this goal. Chapter One covers a stochastic And-Or knowledge representation framework for robotic manipulations. Chapter Two further expands this established system for robustly learning from perception. Chapter Three unifies perception with natural language for a joint real-time processing of information. We've successfully tested the generalizability and faithfulness of our robotic knowledge acquisition and inference pipeline. We present proof of concepts in each of the three chapters.