# Improving Grounded Natural Language Understanding through Human-Robot Dialog

## TL;DR

This paper presents an end-to-end system enabling robots to understand and adapt to natural language commands through dialog, improving grounding and parsing in both virtual and real-world environments.

## Contribution

It introduces a dynamic learning pipeline that uses clarification dialogs to enhance language understanding and concept grounding in robotic systems.

## Key findings

- Successful transfer from virtual to physical robot platform
- Improved language parsing accuracy through dialog-based learning
- Effective grounding of perceptual concepts in real-world tasks

## Abstract

Natural language understanding for robotics can require substantial domain- and platform-specific engineering. For example, for mobile robots to pick-and-place objects in an environment to satisfy human commands, we can specify the language humans use to issue such commands, and connect concept words like red can to physical object properties. One way to alleviate this engineering for a new domain is to enable robots in human environments to adapt dynamically---continually learning new language constructions and perceptual concepts. In this work, we present an end-to-end pipeline for translating natural language commands to discrete robot actions, and use clarification dialogs to jointly improve language parsing and concept grounding. We train and evaluate this agent in a virtual setting on Amazon Mechanical Turk, and we transfer the learned agent to a physical robot platform to demonstrate it in the real world.