The Social Context of Human-Robot Interactions
- URL: http://arxiv.org/abs/2508.13982v1
- Date: Tue, 19 Aug 2025 16:15:58 GMT
- Title: The Social Context of Human-Robot Interactions
- Authors: Sydney Thompson, Kate Candon, Marynel Vázquez,
- Abstract summary: We propose a conceptual model for describing the social context of a human-robot interaction.<n>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.
- Score: 1.9833681107184533
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- Whom to Respond To? A Transformer-Based Model for Multi-Party Social Robot Interaction [4.276453870301421]
We propose a Transformer-based multi-task learning framework to improve the decision-making process of social robots.<n>We construct a novel multi-party HRI dataset that captures real-world complexities, such as gaze misalignment.<n>Our findings contribute to the development of socially intelligent social robots capable of engaging in natural and context-aware multi-party interactions.
arXiv Detail & Related papers (2025-07-15T03:42:14Z) - The Human Robot Social Interaction (HSRI) Dataset: Benchmarking Foundational Models' Social Reasoning [49.32390524168273]
Our work aims to advance the social reasoning of embodied artificial intelligence (AI) agents in real-world social interactions.<n>We introduce a large-scale real-world Human Robot Social Interaction (HSRI) dataset to benchmark the capabilities of language models (LMs) and foundational models (FMs)<n>Our dataset consists of 400 real-world human social robot interaction videos and over 10K annotations, detailing the robot's social errors, competencies, rationale, and corrective actions.
arXiv Detail & Related papers (2025-04-07T06:27:02Z) - Social Genome: Grounded Social Reasoning Abilities of Multimodal Models [61.88413918026431]
Social reasoning abilities are crucial for AI systems to interpret and respond to multimodal human communication and interaction within social contexts.<n>We introduce SOCIAL GENOME, the first benchmark for fine-grained, grounded social reasoning abilities of multimodal models.
arXiv Detail & Related papers (2025-02-21T00:05:40Z) - Advancing Social Intelligence in AI Agents: Technical Challenges and Open Questions [67.60397632819202]
Building socially-intelligent AI agents (Social-AI) is a multidisciplinary, multimodal research goal.
We identify a set of underlying technical challenges and open questions for researchers across computing communities to advance Social-AI.
arXiv Detail & Related papers (2024-04-17T02:57:42Z) - Socially Integrated Navigation: A Social Acting Robot with Deep Reinforcement Learning [0.7864304771129751]
Mobile robots are being used on a large scale in various crowded situations and become part of our society.
Socially acceptable navigation behavior of a mobile robot with individual human consideration is an essential requirement for scalable applications and human acceptance.
We propose a novel socially integrated navigation approach where the robot's social behavior is adaptive and emerges from the interaction with humans.
arXiv Detail & Related papers (2024-03-14T18:25:40Z) - SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents [107.4138224020773]
We present SOTOPIA, an open-ended environment to simulate complex social interactions between artificial agents and humans.
In our environment, agents role-play and interact under a wide variety of scenarios; they coordinate, collaborate, exchange, and compete with each other to achieve complex social goals.
We find that GPT-4 achieves a significantly lower goal completion rate than humans and struggles to exhibit social commonsense reasoning and strategic communication skills.
arXiv Detail & Related papers (2023-10-18T02:27:01Z) - Didn't see that coming: a survey on non-verbal social human behavior
forecasting [47.99589136455976]
Non-verbal social human behavior forecasting has increasingly attracted the interest of the research community in recent years.
Its direct applications to human-robot interaction and socially-aware human motion generation make it a very attractive field.
We define the behavior forecasting problem for multiple interactive agents in a generic way that aims at unifying the fields of social signals prediction and human motion forecasting.
arXiv Detail & Related papers (2022-03-04T18:25:30Z) - Forecasting Nonverbal Social Signals during Dyadic Interactions with
Generative Adversarial Neural Networks [0.0]
Successful social interaction is closely coupled with the interplay between nonverbal perception and action mechanisms.
Nonverbal gestures are expected to endow social robots with the capability of emphasizing their speech, or showing their intentions.
Our research sheds a light on modeling human behaviors in social interactions, specifically, forecasting human nonverbal social signals during dyadic interactions.
arXiv Detail & Related papers (2021-10-18T15:01:32Z) - Simulating Social Acceptability With Agent-based Modeling [28.727916976371265]
We suggest to reframe the social space as a dynamic bundle of social practices.
We outline possible research directions that focus on specific interactions among practices as well as regularities in emerging patterns.
arXiv Detail & Related papers (2021-05-14T09:31:43Z) - PHASE: PHysically-grounded Abstract Social Events for Machine Social
Perception [50.551003004553806]
We create a dataset of physically-grounded abstract social events, PHASE, that resemble a wide range of real-life social interactions.
Phase is validated with human experiments demonstrating that humans perceive rich interactions in the social events.
As a baseline model, we introduce a Bayesian inverse planning approach, SIMPLE, which outperforms state-of-the-art feed-forward neural networks.
arXiv Detail & Related papers (2021-03-02T18:44:57Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.