# Navigational Instruction Generation as Inverse Reinforcement Learning with Neural Machine Translation

arXiv: https://arxiv.org/abs/1610.03164 · HTML: https://mlsanity.com/paper/1610.03164/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2016.

## Built on by (most cited first, 12 of 12)

- [Less is More: Generating Grounded Navigation Instructions from Landmarks](https://mlsanity.com/paper/2111.12872/) (2021)
- [Counterfactual Cycle-Consistent Learning for Instruction Following and Generation in Vision-Language Navigation](https://mlsanity.com/paper/2203.16586/) (2022)
- [Grounding Language to Entities and Dynamics for Generalization in Reinforcement Learning](https://mlsanity.com/paper/2101.07393/) (2021)
- [Lana: A Language-Capable Navigator for Instruction Following and Generation](https://mlsanity.com/paper/2303.08409/) (2023)
- [GPT3-to-plan: Extracting plans from text using GPT-3](https://mlsanity.com/paper/2106.07131/) (2021)
- [SILG: The Multi-environment Symbolic Interactive Language Grounding Benchmark](https://mlsanity.com/paper/2110.10661/) (2021)
- [Language Understanding for Field and Service Robots in a Priori Unknown Environments](https://mlsanity.com/paper/2105.10396/) (2021)
- [Natural Language for Human-Robot Collaboration: Problems Beyond Language Grounding](https://mlsanity.com/paper/2110.04441/) (2021)
- [Spatially-Aware Speaker for Vision-and-Language Navigation Instruction Generation](https://mlsanity.com/paper/2409.05583/) (2024)
- [Reward Shaping for Human Learning via Inverse Reinforcement Learning](https://mlsanity.com/paper/2002.10904/) (2020)
- [Online No-regret Model-Based Meta RL for Personalized Navigation](https://mlsanity.com/paper/2204.01925/) (2022)
- [Time-Aware Assistive Navigation](https://mlsanity.com/paper/2609.05596/) (2026)

## Abstract (arXiv)

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.