Reinforcement learning defines the problem facing agents that learn to make good decisions through action and observation alone. To be effective problem solvers, such agents …
This is an updated version of original (DOI: 10.26300/1pad-7574) that contains minor revisions (see page iv for list of revisions). Reinforcement learning defines the problem …
Crowdsourced training data has become a mainstay in computer vision. Some of the most significant discoveries of the last few years were made possible by …
Coreference Resolution is a fundamental natural language processing (NLP) problem, as it attempts to resolve which underlying discourse objects refer to one another. Further, it …
Trust, and specifically appropriate trust, is essential to beneficial interaction between agents. Robots are becoming increasingly present in everyday life and offer a multitude of …
With few exceptions, robots today are unable to quickly acquire new manipulation skills in the real world. The modern data-driven approach to skill learning is …
In this work, I present an implemented model that can learn interactively from natural language, enabling non-expert human trainers to convey complex tasks to machines …
Conversational assistive robots have the potential to help human users accomplish a wide range of daily tasks, such as cooking meals, performing exercises, or operating …
A natural language parser recovers the latent grammatical structures of sentences. In many natural language processing (NLP) applications, parsing is applied to sentences first and …
Robotic perception is a key step in any autonomous robotic task including manipulation, localization and planning. The more precise the perception system is, the more …