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A Unified Knowledge Representation System for Robot Learning and Dialogue

  • Author(s): Shukla, Nishant
  • Advisor(s): Zhu, Song-Chun
  • et al.
Abstract

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.

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