Navigational Instruction Generation as Inverse Reinforcement Learning with Neural Machine Translation

TL;DR

This paper presents a method for robots to generate natural language navigational instructions by combining inverse reinforcement learning for information sharing policies with neural machine translation, enabling effective human-robot communication.

Contribution

It introduces a novel approach that integrates inverse reinforcement learning and neural translation to generate human-like navigation instructions in unknown environments.

Findings

Achieved a BLEU score of 72.18% on benchmark data.

Instructions are as accurate and easy to follow as human-generated ones.

Demonstrated effective human-robot navigation communication.

Abstract

Modern robotics applications that involve human-robot interaction require robots to be able to communicate with humans seamlessly and effectively. Natural language provides a flexible and efficient medium through which robots can exchange information with their human partners. Significant advancements have been made in developing robots capable of interpreting free-form instructions, but less attention has been devoted to endowing robots with the ability to generate natural language. We propose a navigational guide model that enables robots to generate natural language instructions that allow humans to navigate a priori unknown environments. We first decide which information to share with the user according to their preferences, using a policy trained from human demonstrations via inverse reinforcement learning. We then "translate" this information into a natural language instruction using a neural sequence-to-sequence model that learns to generate free-form instructions from natural language corpora. We evaluate our method on a benchmark route instruction dataset and achieve a BLEU score of 72.18% when compared to human-generated reference instructions. We additionally conduct navigation experiments with human participants that demonstrate that our method generates instructions that people follow as accurately and easily as those produced by humans.