UCLA

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.