[论文解读] A Deep Neural Model Of Emotion Appraisal

Pablo Barros, Emilia Barakova+1 more|arXiv (Cornell University)|Aug 1, 2018

Emotion and Mood Recognition参考文献 69被引用 9

一句话总结

本文提出一种用于社交机器人情感评估的深度神经模型，通过混合神经架构整合跨模态感知、情感记忆与动态情绪状态表示。该模型能够从人机交互中持续学习情感概念，在情感识别方面表现优异，并可生成随时间演化的内部情绪状态，已在iCub机器人平台上部署并基于新型人机交互数据集得到验证。

ABSTRACT

Emotional concepts play a huge role in our daily life since they take part into many cognitive processes: from the perception of the environment around us to different learning processes and natural communication. Social robots need to communicate with humans, which increased also the popularity of affective embodied models that adopt different emotional concepts in many everyday tasks. However, there is still a gap between the development of these solutions and the integration and development of a complex emotion appraisal system, which is much necessary for true social robots. In this paper, we propose a deep neural model which is designed in the light of different aspects of developmental learning of emotional concepts to provide an integrated solution for internal and external emotion appraisal. We evaluate the performance of the proposed model with different challenging corpora and compare it with state-of-the-art models for external emotion appraisal. To extend the evaluation of the proposed model, we designed and collected a novel dataset based on a Human-Robot Interaction (HRI) scenario. We deployed the model in an iCub robot and evaluated the capability of the robot to learn and describe the affective behavior of different persons based on observation. The performed experiments demonstrate that the proposed model is competitive with the state of the art in describing emotion behavior in general. In addition, it is able to generate internal emotional concepts that evolve through time: it continuously forms and updates the formed emotional concepts, which is a step towards creating an emotional appraisal model grounded in the robot experiences.

研究动机与目标

• 开发一种集成的、自适应的情感评估系统，支持外部（感知）与内部（情绪、情感记忆）双重评估。
• 弥合现有情感模型与机器人长期、基于经验的情感理解需求之间的差距。
• 使机器人能够通过观察真实世界中的人机交互场景，学习并描述个性化的感情行为。
• 构建一个新型数据集与评估方法论，用于评估人机交互中长期情感行为建模。
• 展示模型在长期过程中形成并更新内部情感概念的能力，从而增强社交适应性。

提出的方法

• 模型采用跨通道卷积神经网络（PerceptionGWR）实现多模态情感感知，支持从视觉与听觉输入中聚类情感概念。
• 采用按需生长的循环网络架构，实现在无灾难性遗忘情况下的情感类别持续、增量式学习。
• 情感记忆与情绪记忆模块通过调制连接集成，基于感知刺激动态更新情感表征。
• 通过概念更新与调制反馈，实现在单模态与多模态条件下的学习稳定性。
• 系统部署于iCub机器人上，用于在受控人机交互场景中观察并学习人类互动中的情感行为。
• 收集了一个新型基于人机交互的数据集，用于评估长期情感行为建模，并设计了用于内在情绪形成评估的定制化方法论。

实验结果

研究问题

• RQ1 深度神经模型能否有效整合跨模态感知与内在情感表征，实现在机器人中的持续情感评估？
• RQ2 与现有最先进模型相比，该模型在标准语料上的外部情感评估性能如何？
• RQ3 该模型在长期人机交互中，通过观察能多大程度上学习并表征个性化的感情行为？
• RQ4 情绪记忆如何调制情感记忆，其对情感表征稳定性与结构的影响如何？
• RQ5 该模型能否生成反映实时、累积人类交互体验的演化内部情绪状态？

主要发现

• 所提出的模型在外部情感评估方面表现具有竞争力，在标准语料上达到或超过现有最先进模型的性能。
• 该模型成功基于观察到的人机交互持续形成并更新内部情感概念，展现出长期适应能力。
• 情绪记忆模块在不显著改变其拓扑结构的前提下调制情感记忆，从而在时间上保持了情感上下文的一致性。
• 在人机交互场景中，该模型有效学习并描述了不同个体的独特情感行为，且在较短、表达较弱的互动中观察到更强的情绪调制效应。
• 新型人机交互数据集与评估框架为评估社交机器人长期情感行为建模提供了可靠的基准。
• 情感记忆与情绪调制的集成实现了情感状态的稳定、演化表征，持续时间可达数分钟的交互。