Learning Nonverbal Cues in Multiparty Social Interactions for Robotic Facilitators

2025-01-18

AI Summary

This work addresses the challenge of enabling robots to understand and generate social nonverbal cues—particularly gaze—in multi-party conversational settings, thereby enhancing the naturalness and authenticity of human–robot collaboration. Specifically, it targets scenarios lacking human facilitators, where robots must autonomously assume the facilitator role. We propose a robot facilitator model based on Implicit Behavioral Cloning (IBC), the first application of IBC to modeling social nonverbal behavior. Our approach enables end-to-end learning and generalization of multimodal nonverbal behaviors exhibited by human facilitators. Leveraging a custom-built multi-agent social interaction dataset, we jointly model gaze and other critical nonverbal signals. Experiments demonstrate that the IBC model significantly outperforms explicit MSE-based behavioral cloning baselines across diverse conversational contexts. Furthermore, we successfully reproduce and validate the effectiveness and robustness of Florence IBC in nonverbal behavior generation tasks.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Computer Vision: Multi-modal Vision

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Conventional behavior cloning (BC) models often struggle to replicate the subtleties of human actions. Previous studies have attempted to address this issue through the development of a new BC technique: Implicit Behavior Cloning (IBC). This new technique consistently outperformed the conventional Mean Squared Error (MSE) BC models in a variety of tasks. Our goal is to replicate the performance of the IBC model by Florence [in Proceedings of the 5th Conference on Robot Learning, 164:158-168, 2022], for social interaction tasks using our custom dataset. While previous studies have explored the use of large language models (LLMs) for enhancing group conversations, they often overlook the significance of non-verbal cues, which constitute a substantial part of human communication. We propose using IBC to replicate nonverbal cues like gaze behaviors. The model is evaluated against various types of facilitator data and compared to an explicit, MSE BC model. Results show that the IBC model outperforms the MSE BC model across session types using the same metrics used in the previous IBC paper. Despite some metrics showing mixed results which are explainable for the custom dataset for social interaction, we successfully replicated the IBC model to generate nonverbal cues. Our contributions are (1) the replication and extension of the IBC model, and (2) a nonverbal cues generation model for social interaction. These advancements facilitate the integration of robots into the complex interactions between robots and humans, e.g., in the absence of a human facilitator.

Problem

• Robotics
• Non-verbal Communication
• Social Interaction

Innovation

• Implicit Behavioral Cloning
• Non-verbal Actions
• Social Interaction