论文标题
超级参数优化，用于基于深度学习的人类机器人互动的情绪预测
Hyperparameters optimization for Deep Learning based emotion prediction for Human Robot Interaction
论文作者
论文摘要
为了使人形机器人能够共享我们的社会空间，我们需要开发技术，以便使用多种模式（例如语音，手势）与机器人互动，并与他们分享我们的情绪。我们将这项研究的目标是解决情绪识别问题的核心问题，该问题需要更少的计算资源和较少的网络超参数数量，这将更具适应性，以便在低资源的社交机器人上进行实时通信。更具体地说，在这里，我们提出了一个基于Inception模块的卷积神经网络体系结构，该架构的准确性比现有网络体系结构的精度提高了6％，当在实际尝试对类人体机器人的情况下，对现有网络体系结构进行了情感分类。与香草CNN模型相比，我们提出的模型将可训练的超参数降低到94％的范围，这清楚地表明它可以在实时应用中使用，例如人类机器人的相互作用。进行了严格的实验来验证我们的方法学，该方法足够强大，可以达到高度的准确性。最后，该模型是在人形机器人中实现的，实时NAO，并评估模型的鲁棒性。
To enable humanoid robots to share our social space we need to develop technology for easy interaction with the robots using multiple modes such as speech, gestures and share our emotions with them. We have targeted this research towards addressing the core issue of emotion recognition problem which would require less computation resources and much lesser number of network hyperparameters which will be more adaptive to be computed on low resourced social robots for real time communication. More specifically, here we have proposed an Inception module based Convolutional Neural Network Architecture which has achieved improved accuracy of upto 6% improvement over the existing network architecture for emotion classification when combinedly tested over multiple datasets when tried over humanoid robots in real - time. Our proposed model is reducing the trainable Hyperparameters to an extent of 94% as compared to vanilla CNN model which clearly indicates that it can be used in real time based application such as human robot interaction. Rigorous experiments have been performed to validate our methodology which is sufficiently robust and could achieve high level of accuracy. Finally, the model is implemented in a humanoid robot, NAO in real time and robustness of the model is evaluated.
