S.E.A.G.R: A Socially and Emotionally Aware Greeting Robot Framework with Dual-Layer Cultural and Affective Modulation
This paper presents SEAGR, a robot greeting framework for users from diverse cultural backgrounds and emotional states. It uses a dual-layer modulation where cultural identity determines greeting type and affective cues modulate execution, integrating cultural mapping, emotion-based gestures, and proxemics in a Sense-Think-Act architecture. A low-cost prototype is built; however, empirical validation via user studies is currently lacking.
-->
[Submitted on 16 Jul 2026]
Title:S.E.A.G.R: A Socially and Emotionally Aware Greeting Robot Framework with Dual-Layer Cultural and Affective Modulation
View a PDF of the paper titled S.E.A.G.R: A Socially and Emotionally Aware Greeting Robot Framework with Dual-Layer Cultural and Affective Modulation, by Sajjad Hussain and 3 other authors
View PDF HTML (experimental)
Abstract:This paper presents SEAGR (Socially and Emotionally Aware Greeting Robot), a robotic greeting framework designed for human-robot interaction environments involving users from diverse cultural backgrounds and different emotional states. Since greeting behaviour strongly influences first impressions, user comfort, and trust, robots operating in public spaces must be able to interact in a socially appropriate and adaptive manner. However, many existing systems still rely on static greeting routines that do not account for cultural variation, emotional context, or interpersonal distance. SEAGR introduces a dual-layer modulation framework in which cultural identity determines the appropriate greeting type, while affective cues influence how that greeting is executed. The system combines context-aware cultural mapping, emotion-based gesture modulation, and proxemic regulation within a unified Sense-Think-Act architecture. A low-cost prototype is implemented using a USB camera, ultrasonic sensor, Arduino-controlled servos, and a laptop-based Python processing system. This work is presented as a system design and proof-of-concept; empirical validation through user studies is explicitly acknowledged as a current limitation and is identified as the primary direction for future work.
Comments: 13 pages, 6 figures. Accepted for publication in the proceedings of the 18th International Conference on Social Robotics (ICSR 2026)
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2607.16341 [cs.RO]
(or arXiv:2607.16341v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.16341
arXiv-issued DOI via DataCite
Submission history
From: Sajjad Hussain Mr [view email] [v1] Thu, 16 Jul 2026 17:15:59 UTC (15 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled S.E.A.G.R: A Socially and Emotionally Aware Greeting Robot Framework with Dual-Layer Cultural and Affective Modulation, by Sajjad Hussain and 3 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.RO
new | recent | 2026-07
Change to browse by:
cs
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)