# A Review of Methodologies for Natural-Language-Facilitated Human-Robot Cooperation

arXiv: https://arxiv.org/abs/1701.08756 · HTML: https://mlsanity.com/paper/1701.08756/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2017.

## Built on by (most cited first, 8 of 8)

- [AutoGPT+P: Affordance-based Task Planning with Large Language Models](https://mlsanity.com/paper/2402.10778/) (2024)
- [DRAGON: A Dialogue-Based Robot for Assistive Navigation with Visual Language Grounding](https://mlsanity.com/paper/2307.06924/) (2023)
- [Talk-to-Resolve: Combining scene understanding and spatial dialogue to resolve granular task ambiguity for a collocated robot](https://mlsanity.com/paper/2111.11099/) (2021)
- [Learning Visual-Audio Representations for Voice-Controlled Robots](https://mlsanity.com/paper/2109.02823/) (2021)
- [Natural Language Specification of Reinforcement Learning Policies through Differentiable Decision Trees](https://mlsanity.com/paper/2101.07140/) (2021)
- [Exploring Large Language Models to Facilitate Variable Autonomy for Human-Robot Teaming](https://mlsanity.com/paper/2312.07214/) (2023)
- [DASH: Modularized Human Manipulation Simulation with Vision and Language for Embodied AI](https://mlsanity.com/paper/2108.12536/) (2021)
- [NSP: A Neuro-Symbolic Natural Language Navigational Planner](https://mlsanity.com/paper/2409.06859/) (2024)

## Abstract (arXiv)

Natural-language-facilitated human-robot cooperation (NLC) refers to using natural language (NL) to facilitate interactive information sharing and task executions with a common goal constraint between robots and humans. Recently, NLC research has received increasing attention. Typical NLC scenarios include robotic daily assistance, robotic health caregiving, intelligent manufacturing, autonomous navigation, and robot social accompany. However, a thorough review, that can reveal latest methodologies to use NL to facilitate human-robot cooperation, is missing. In this review, a comprehensive summary about methodologies for NLC is presented. NLC research includes three main research focuses: NL instruction understanding, NL-based execution plan generation, and knowledge-world mapping. In-depth analyses on theoretical methods, applications, and model advantages and disadvantages are made. Based on our paper review and perspective, potential research directions of NLC are summarized.