The Emotionally Intelligent Robot: Improving Socially-aware Human Prediction in Crowded Environments
The Emotionally Intelligent Robot: Improving Socially-aware Human Prediction in Crowded Environments
复制标题
情商机器人：改善拥挤环境中具有社交意识的人类预测
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Dinesh Manocha
中科院分区：
文献类型：
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作者：
Aniket Bera;Tanmay Randhavane;Dinesh Manocha
提出了一种机器人在行人中进行情感感知导航的实时算法。我们的方法结合了贝叶斯推理、基于CNN的学习和心理学中的PAD(愉悦-ArousDomance)模型，根据行人的面部和轨迹估计了行人的时变情感行为。这些PAD特征被用于长期路径预测，并为每个行人生成代理约束。我们使用多通道模型将行人特征分为四种情绪类别(高兴、悲伤、愤怒、中立)。在我们的验证结果中，我们观察到了85.33%的情感检测准确率。我们制定了基于情感的代理约束，以在中低密度环境中执行社会性感知的机器人导航。我们在数十个行人的模拟环境中以及在社交类人机器人Pepper的真实环境中演示了我们的算法的好处。
We present a real-time algorithm for emotion-aware navigation of a robot among pedestrians. Our approach estimates time-varying emotional behaviors of pedestrians from their faces and trajectories using a combination of Bayesianinference, CNN-based learning, and the PAD (Pleasure-ArousalDominance) model from psychology. These PAD characteristics are used for long-term path prediction and generating proxemic constraints for each pedestrian. We use a multi-channel model to classify pedestrian characteristics into four emotion categories (happy, sad, angry, neutral). In our validation results, we observe an emotion detection accuracy of 85.33%. We formulate emotion-based proxemic constraints to perform socially-aware robot navigation in lowto medium-density environments. We demonstrate the benefits of our algorithm in simulated environments with tens of pedestrians as well as in a real-world setting with Pepper, a social humanoid robot.