SEAGR robot framework adapts greetings to culture and emotion
Dual-layer system reads cultural norms and affective cues for personalized robot greetings
A new paper from Hussain, Bhura, Dutta, and Biswas introduces SEAGR, a robotic greeting framework designed for human-robot interaction in culturally diverse public spaces. Unlike static greeting routines common in existing robots, SEAGR uses a dual-layer modulation approach: the first layer maps the user’s cultural identity to an appropriate greeting type (e.g., bowing vs. handshake), while the second layer adapts the greeting’s execution—such as gesture intensity, eye contact, and voice tone—based on real-time emotional cues detected from the user’s face and posture. This is achieved through a Sense-Think-Act architecture that integrates a USB camera, ultrasonic sensor for proxemic distance, and Arduino-controlled servos for physical gestures, all running on a Python-based control system.
SEAGR is presented as a system design and proof-of-concept; the authors explicitly note the lack of empirical user studies as a current limitation, with planned future work including cross-cultural validation and real-world deployment in airports or hospitals. The low-cost hardware (under $200) makes it accessible for research labs and small-scale deployments. While still experimental, SEAGR offers a promising step toward socially adaptive robots that can reduce awkwardness and build trust across diverse populations.
- Dual-layer framework: cultural identity selects greeting type, affective state modulates execution
- Low-cost prototype: USB camera, ultrasonic sensor, Arduino servos, Python processing (<$200)
- Explicitly a proof-of-concept pending empirical validation in future user studies
Why It Matters
Enables robots to greet users appropriately across cultures and emotional states, improving trust and first impressions.