Transferring Expert Cognitive Models to Social Robots via Agentic Concept Bottleneck Models

• URL: http://arxiv.org/abs/2508.03998v1
• Date: Wed, 06 Aug 2025 01:24:06 GMT
• Title: Transferring Expert Cognitive Models to Social Robots via Agentic Concept Bottleneck Models
• Authors: Xinyu Zhao, Zhen Tan, Maya Enisman, Minjae Seo, Marta R. Durantini, Dolores Albarracin, Tianlong Chen,
• Abstract summary: We develop a social robot co-facilitator that analyzes multimodal meeting data and provides discreet cues to the facilitator.<n>Our core contribution is a transfer learning framework that distills the broad social understanding of an FM into our specialized and transparent CBM.
• Score: 30.8887488736873
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Successful group meetings, such as those implemented in group behavioral-change programs, work meetings, and other social contexts, must promote individual goal setting and execution while strengthening the social relationships within the group. Consequently, an ideal facilitator must be sensitive to the subtle dynamics of disengagement, difficulties with individual goal setting and execution, and interpersonal difficulties that signal a need for intervention. The challenges and cognitive load experienced by facilitators create a critical gap for an embodied technology that can interpret social exchanges while remaining aware of the needs of the individuals in the group and providing transparent recommendations that go beyond powerful but "black box" foundation models (FMs) that identify social cues. We address this important demand with a social robot co-facilitator that analyzes multimodal meeting data and provides discreet cues to the facilitator. The robot's reasoning is powered by an agentic concept bottleneck model (CBM), which makes decisions based on human-interpretable concepts like participant engagement and sentiments, ensuring transparency and trustworthiness. Our core contribution is a transfer learning framework that distills the broad social understanding of an FM into our specialized and transparent CBM. This concept-driven system significantly outperforms direct zero-shot FMs in predicting the need for intervention and enables real-time human correction of its reasoning. Critically, we demonstrate robust knowledge transfer: the model generalizes across different groups and successfully transfers the expertise of senior human facilitators to improve the performance of novices. By transferring an expert's cognitive model into an interpretable robotic partner, our work provides a powerful blueprint for augmenting human capabilities in complex social domains.

Related papers

• When Models Know More Than They Can Explain: Quantifying Knowledge Transfer in Human-AI Collaboration [79.69935257008467]
  We introduce Knowledge Integration and Transfer Evaluation (KITE), a conceptual and experimental framework for Human-AI knowledge transfer capabilities.<n>We conduct the first large-scale human study (N=118) explicitly designed to measure it.<n>In our two-phase setup, humans first ideate with an AI on problem-solving strategies, then independently implement solutions, isolating model explanations' influence on human understanding.
  arXiv  Detail & Related papers  (2025-06-05T20:48:16Z)
• The Traitors: Deception and Trust in Multi-Agent Language Model Simulations [0.0]
  We introduce The Traitors, a multi-agent simulation framework inspired by social deduction games.<n>We develop a suite of evaluation metrics capturing deception success, trust dynamics, and collective inference quality.<n>Our initial experiments across DeepSeek-V3, GPT-4o-mini, and GPT-4o (10 runs per model) reveal a notable asymmetry.
  arXiv  Detail & Related papers  (2025-05-19T10:01:35Z)
• 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)
• Building Knowledge from Interactions: An LLM-Based Architecture for Adaptive Tutoring and Social Reasoning [42.09560737219404]
  Large Language Models show promise in human-like communication, but their standalone use is hindered by memory constraints and contextual incoherence.<n>This work presents a multimodal, cognitively inspired framework that enhances LLM-based autonomous decision-making in social and task-oriented Human-Robot Interaction.<n>To further enhance autonomy and personalization, we introduce a memory system for selecting, storing and retrieving experiences.
  arXiv  Detail & Related papers  (2025-04-02T10:45:41Z)
• Constrained Human-AI Cooperation: An Inclusive Embodied Social Intelligence Challenge [47.74313897705183]
  CHAIC is an inclusive embodied social intelligence challenge designed to test social perception and cooperation in embodied agents.<n>In CHAIC, the goal is for an embodied agent equipped with egocentric observations to assist a human who may be operating under physical constraints.
  arXiv  Detail & Related papers  (2024-11-04T04:41:12Z)
• Latent-Predictive Empowerment: Measuring Empowerment without a Simulator [56.53777237504011]
  We present Latent-Predictive Empowerment (LPE), an algorithm that can compute empowerment in a more practical manner. LPE learns large skillsets by maximizing an objective that is a principled replacement for the mutual information between skills and states.
  arXiv  Detail & Related papers  (2024-10-15T00:41:18Z)
• A Multi-Modal Explainability Approach for Human-Aware Robots in Multi-Party Conversation [38.227022474450834]
  We present an addressee estimation model with improved performance in comparison with the previous state-of-the-art.<n>We also propose several ways to incorporate explainability and transparency in the aforementioned architecture.
  arXiv  Detail & Related papers  (2024-05-20T13:09:32Z)
• Extended Reality for Enhanced Human-Robot Collaboration: a Human-in-the-Loop Approach [2.336967926255341]
  Human-robot collaboration attempts to tackle these challenges by combining the strength and precision of machines with human ingenuity and perceptual understanding. We propose an implementation framework for an autonomous, machine learning-based manipulator that incorporates human-in-the-loop principles. The conceptual framework foresees human involvement directly in the robot learning process, resulting in higher adaptability and task generalization.
  arXiv  Detail & Related papers  (2024-03-21T17:50:22Z)
• Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation [50.01551945190676]
  Social robot navigation can be helpful in various contexts of daily life but requires safe human-robot interactions and efficient trajectory planning. We propose a systematic relational reasoning approach with explicit inference of the underlying dynamically evolving relational structures. We demonstrate its effectiveness for multi-agent trajectory prediction and social robot navigation.
  arXiv  Detail & Related papers  (2024-01-22T18:58:22Z)
• Towards socially-competent and culturally-adaptive artificial agents Expressive order, interactional disruptions and recovery strategies [0.0]
  The overarching aim of this work is to set a framework to make the artificial agent socially-competent beyond dyadic interaction-interaction. The paper highlights how this level of competence is achieved by focusing on just three dimensions: (i) social capability, (ii) relational role, and (iii) proximity.
  arXiv  Detail & Related papers  (2023-08-06T15:47:56Z)
• You Impress Me: Dialogue Generation via Mutual Persona Perception [62.89449096369027]
  The research in cognitive science suggests that understanding is an essential signal for a high-quality chit-chat conversation. Motivated by this, we propose P2 Bot, a transmitter-receiver based framework with the aim of explicitly modeling understanding.
  arXiv  Detail & Related papers  (2020-04-11T12:51:07Z)

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.