# Nonverbal Cues in Human-Robot Interaction: A Communication Studies Perspective

arXiv: https://arxiv.org/abs/2304.11293 · HTML: https://mlsanity.com/paper/2304.11293/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 153 references that are in the map.
- First posted on arXiv 22 Apr 2023.

## Builds on (most cited first, 4 of 4)

- [What Pronouns for Pepper? A Critical Review of Gender/ing in Research](https://mlsanity.com/paper/2212.04206/) (2022)
- [Measuring Voice UX Quantitatively: A Rapid Review](https://mlsanity.com/paper/2103.07108/) (2021)
- [Crossing the Tepper Line: An Emerging Ontology for Describing the Dynamic Sociality of Embodied AI](https://mlsanity.com/paper/2103.08079/) (2021)
- [Establishing Human-Robot Trust through Music-Driven Robotic Emotion Prosody and Gesture](https://mlsanity.com/paper/2001.05863/) (2020)

## Built on by (most cited first, 16 of 16)

- [EMOTION: Expressive Motion Sequence Generation for Humanoid Robots with In-Context Learning](https://mlsanity.com/paper/2410.23234/) (2024)
- [Predicting the Intention to Interact with a Service Robot:the Role of Gaze Cues](https://mlsanity.com/paper/2404.01986/) (2024)
- [HRIBench: Benchmarking Vision-Language Models for Real-Time Human Perception in Human-Robot Interaction](https://mlsanity.com/paper/2506.20566/) (2025)
- [AV-EmoDialog: Chat with Audio-Visual Users Leveraging Emotional Cues](https://mlsanity.com/paper/2412.17292/) (2024)
- [Social Identity in Human-Agent Interaction: A Primer](https://mlsanity.com/paper/2508.16609/) (2025)
- [PolySLGen: Online Multimodal Speaking-Listening Reaction Generation in Polyadic Interaction](https://mlsanity.com/paper/2604.08125/) (2026)
- [SLYKLatent: A Learning Framework for Gaze Estimation Using Deep Facial Feature Learning](https://mlsanity.com/paper/2402.01555/) (2024)
- [Qualitative Approaches to Voice UX](https://mlsanity.com/paper/2404.14736/) (2024)
- [ChatGPT and U(X): A Rapid Review on Measuring the User Experience](https://mlsanity.com/paper/2503.15808/) (2025)
- [Non-verbal Real-time Human-AI Interaction in Constrained Robotic Environments](https://mlsanity.com/paper/2603.01804/) (2026)
- [Unveiling the Limits of Large Language Models in Inferring Pragmatic Meaning from Non-Verbal Responses](https://mlsanity.com/paper/2606.01845/) (2026)
- [QUB-PHEO: A Visual-Based Dyadic Multi-View Dataset for Intention Inference in Collaborative Assembly](https://mlsanity.com/paper/2409.15560/) (2024)
- [DiG-Net: Enhancing Human-Robot Interaction through Hyper-Range Dynamic Gesture Recognition in Assistive Robotics](https://mlsanity.com/paper/2505.24786/) (2025)
- [Operationalizing Perceptions of Agent Gender: Foundations and Guidelines](https://mlsanity.com/paper/2603.26682/) (2026)
- [Prompt-to-Gesture: Measuring the Capabilities of Image-to-Video Deictic Gesture Generation](https://mlsanity.com/paper/2604.14953/) (2026)
- [Real-time body pose non-verbal communication with a consistency-based reliability measure](https://mlsanity.com/paper/2606.09390/) (2026)

## Abstract (arXiv)

Communication between people is characterized by a broad range of nonverbal cues. Transferring these cues into the design of robots and other artificial agents that interact with people may foster more natural, inviting, and accessible experiences. In this position paper, we offer a series of definitive nonverbal codes for human-robot interaction (HRI) that address the five human sensory systems (visual, auditory, haptic, olfactory, gustatory) drawn from the field of communication studies. We discuss how these codes can be translated into design patterns for HRI using a curated sample of the communication studies and HRI literatures. As nonverbal codes are an essential mode in human communication, we argue that integrating robotic nonverbal codes in HRI will afford robots a feeling of "aliveness" or "social agency" that would otherwise be missing. We end with suggestions for research directions to stimulate work on nonverbal communication within the field of HRI and improve communication between human and robots.