USER-VLM 360: Personalized Vision Language Models with User-aware Tuning for Social Human-Robot Interactions

• URL: http://arxiv.org/abs/2502.10636v1
• Date: Sat, 15 Feb 2025 02:25:49 GMT
• Title: USER-VLM 360: Personalized Vision Language Models with User-aware Tuning for Social Human-Robot Interactions
• Authors: Hamed Rahimi, Adil Bahaj, Mouad Abrini, Mahdi Khoramshahi, Mounir Ghogho, Mohamed Chetouani,
• Abstract summary: We propose User-VLM 360deg, a holistic framework integrating multimodal user modeling with bias-aware optimization. Our approach features: (1) user-aware tuning that adapts interactions in real time using visual-linguistic signals; (2) bias mitigation via preference optimization; and (3) curated 360deg socio-emotive interaction datasets annotated with demographic, emotion, and relational metadata.
• Score: 6.2486440301992605
• License:
• Abstract: The integration of vision-language models into robotic systems constitutes a significant advancement in enabling machines to interact with their surroundings in a more intuitive manner. While VLMs offer rich multimodal reasoning, existing approaches lack user-specific adaptability, often relying on generic interaction paradigms that fail to account for individual behavioral, contextual, or socio-emotional nuances. When customization is attempted, ethical concerns arise from unmitigated biases in user data, risking exclusion or unfair treatment. To address these dual challenges, we propose User-VLM 360{\deg}, a holistic framework integrating multimodal user modeling with bias-aware optimization. Our approach features: (1) user-aware tuning that adapts interactions in real time using visual-linguistic signals; (2) bias mitigation via preference optimization; and (3) curated 360{\deg} socio-emotive interaction datasets annotated with demographic, emotion, and relational metadata. Evaluations across eight benchmarks demonstrate state-of-the-art results: +35.3% F1 in personalized VQA, +47.5% F1 in facial features understanding, 15% bias reduction, and 30X speedup over baselines. Ablation studies confirm component efficacy, and deployment on the Pepper robot validates real-time adaptability across diverse users. We open-source parameter-efficient 3B/10B models and an ethical verification framework for responsible adaptation.

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