This paper presents a proposal for the identification of multimodal signals for recognizing 4 human emotions in the context of human-robot interaction, specifically, the following emotions: happiness, anger, surprise and neutrality. We propose to implement a multiclass classifier that is based on two unimodal classifiers: one to process the input data from a video signal and another one that uses audio. On one hand, for detecting the human emotions using video data we have propose a multiclass image classifier based on a convolutional neural network that achieved 86.4% of generalization accuracy for individual frames and 100% when used to detect emotions in a video stream. On the other hand, for the emotion detection using audio data we have proposed a multiclass classifier based on several one-class classifiers, one for each emotion, achieving a generalization accuracy of 69.7% . The complete system shows a generalization error of 0% and is tested with several real users in an sales-robot application.
Plenty of work based on the Rapidly-exploring Random Trees (RRT) algorithm for path planning in real time has been developed recently. This is the most used algorithm by the top research teams in the Small Size League of RoboCup. Nevertheless, we have concluded that other simpler alternatives show better results under these highly dynamic environments. In this work, we propose a new path planning algorithm that meets all the robotic soccer challenges requirements, which has already been implemented in the STOx’s team for the RoboCup competition in 2013. We have evaluated the algorithm’s performance using metrics such as the smoothness of the paths, the traveled distance and the processing time and compared it with the RRT algorithm’s. The results showed improved performance over RRT when combined measures are used.
In this paper we propose a methodology to build multiclass classifiers for the human-robot interaction problem. Our solution uses kernel-based classifiers and assumes that each data type is better represented by a different kernel. The kernels are then combined into one single kernel that uses all the dataset involved in the HRI process. The results on real data shows that our proposal is capable of obtaining lower generalization errors due to the use of specific kernels for each data type. Also, we show that our proposal is more robust when presented to noise in either or both data types..
This work shows the design, implementation and evaluation of a human-robot interaction system where a robot is capable of learning multimodal instructions through gestures and voice issued by a human user. The learning procedure can be performed in two ways: an instruction learning phase, where the human aims at teaching one instruction to the robot by performing several repetitions and an instruction receiving phase where the robot reacts to the instructions given by the human and possibly asks for feedback from the user to strengthen the instruction’s model.
This paper shows the results of applying machine learning techniques to the problem of predicting soccer plays in the Small Size League of RoboCup. We have modeled the task as a multi-class classification problem by learning the plays of the STOx’s team. For this, we have created a database of observations for this team’s plays and obtained key features that describe the game state during a match. We have shown experimentally, that these features allow two learning classifiers to obtain high prediction accuracies and that most miss-classified observations are found early on the plays.
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