Clarifying Commands with Information-Theoretic Human-Robot Dialog

Clarifying Commands with Information-Theoretic Human-Robot Dialog

Our goal is to improve the efficiency and effectiveness of natural language communication between humans and robots. Human language is frequently ambiguous, and a robot’s limited sensing makes complete understanding of a statement even more difficult. To address these challenges, we describe an approach for enabling a robot to engage in clarifying dialog with a human partner, just as a human mi...

Toward Information Theoretic Human-Robot Dialog

Our goal is to build robots that can robustly interact with humans using natural language. This problem is challenging because human language is filled with ambiguity, and furthermore, due to limitations in sensing, the robot’s perception of its environment might be much more limited than that of its human partner. To enable a robot to recover from a failure to understand a natural language utt...

Learning to Interpret Natural Language Commands through Human-Robot Dialog

Intelligent robots frequently need to understand requests from naive users through natural language. Previous approaches either cannot account for language variation, e.g., keyword search, or require gathering large annotated corpora, which can be expensive and cannot adapt to new variation. We introduce a dialog agent for mobile robots that understands human instructions through semantic parsi...

An Information Theoretic Measure for Robot Expressivity

This paper presents a principled way to think about articulated movement for artificial agents and a measurement of platforms that produce such movement. In particular, in human-facing scenarios, the shape evolution of robotic platforms will become essential in creating systems that integrate – and communicate – with human counterparts. This paper provides a tool to measure the expressive capac...

Human-Robot Cooperative Sweeping Using Commands Embedded in Actions