# The Social Context of Human-Robot Interactions

arXiv: https://arxiv.org/abs/2508.13982 · HTML: https://mlsanity.com/paper/2508.13982/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 137 references that are in the map.
- First posted on arXiv 19 Aug 2025.

## Builds on (most cited first, 21 of 21)

- [OpenVLA: An Open-Source Vision-Language-Action Model](https://mlsanity.com/paper/2406.09246/) (2024)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [Open X-Embodiment: Robotic Learning Datasets and RT-X Models](https://mlsanity.com/paper/2310.08864/) (2023)
- [The EMPATHIC Framework for Task Learning from Implicit Human Feedback](https://mlsanity.com/paper/2009.13649/) (2020)
- [Augmented Reality and Robotics: A Survey and Taxonomy for AR-enhanced Human-Robot Interaction and Robotic Interfaces](https://mlsanity.com/paper/2203.03254/) (2022)
- [Enhancing Causal Discovery from Robot Sensor Data in Dynamic Scenarios](https://mlsanity.com/paper/2302.10135/) (2023)
- [Causal Discovery of Dynamic Models for Predicting Human Spatial Interactions](https://mlsanity.com/paper/2210.16535/) (2022)
- [AVGCN: Trajectory Prediction using Graph Convolutional Networks Guided by Human Attention](https://mlsanity.com/paper/2101.05682/) (2021)
- [Social-PatteRNN: Socially-Aware Trajectory Prediction Guided by Motion Patterns](https://mlsanity.com/paper/2209.05649/) (2022)
- [It Takes Two: Learning to Plan for Human-Robot Cooperative Carrying](https://mlsanity.com/paper/2209.12890/) (2022)
- [Action Sequence Predictions of Vehicles in Urban Environments using Map and Social Context](https://mlsanity.com/paper/2004.14251/) (2020)
- [An Approach to Deploy Interactive Robotic Simulators on the Web for HRI Experiments: Results in Social Robot Navigation](https://mlsanity.com/paper/2012.12336/) (2020)
- [Graph Neural Networks for Human-aware Social Navigation](https://mlsanity.com/paper/1909.09003/) (2019)
- [Virtual, Augmented, and Mixed Reality for Human-Robot Interaction: A Survey and Virtual Design Element Taxonomy](https://mlsanity.com/paper/2202.11249/) (2022)
- [Learning Coordination Policies over Heterogeneous Graphs for Human-Robot Teams via Recurrent Neural Schedule Propagation](https://mlsanity.com/paper/2301.13279/) (2023)
- [Robot Learning via Human Adversarial Games](https://mlsanity.com/paper/1903.00636/) (2019)
- [Learning Whole-Body Human-Robot Haptic Interaction in Social Contexts](https://mlsanity.com/paper/2005.12508/) (2020)
- [A Deep Learning Approach To Multi-Context Socially-Aware Navigation](https://mlsanity.com/paper/2104.10197/) (2021)
- [SocNavGym: A Reinforcement Learning Gym for Social Navigation](https://mlsanity.com/paper/2304.14102/) (2023)
- [Robot Navigation in Risky, Crowded Environments: Understanding Human Preferences](https://mlsanity.com/paper/2303.08284/) (2023)
- [Predicting Human Perceptions of Robot Performance During Navigation Tasks](https://mlsanity.com/paper/2310.11590/) (2023)

## Built on by (most cited first, 1 of 1)

- [STARS: From Spatiotemporal Dynamics to Social Representations in Human-Robot Interaction](https://mlsanity.com/paper/2609.40245/) (2026)

## Abstract (arXiv)

The Human-Robot Interaction (HRI) community often highlights the social context of an interaction as a key consideration when designing, implementing, and evaluating robot behavior. Unfortunately, researchers use the term "social context" in varied ways. This can lead to miscommunication, making it challenging to draw connections between related work on understanding and modeling the social contexts of human-robot interactions. To address this gap, we survey the HRI literature for existing definitions and uses of the term "social context". Then, we propose a conceptual model for describing the social context of a human-robot interaction. We apply this model to existing work, and we discuss a range of attributes of social contexts that can help researchers plan for interactions, develop behavior models for robots, and gain insights after interactions have taken place. We conclude with a discussion of open research questions in relation to understanding and modeling the social contexts of human-robot interactions.