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This study introduces a method to enable a robot to learn how to perform new tasks through human demonstration and independent practice. The proposed process consists of two interconnected phases; in the first phase, state-action data are obtained from human demonstrations, and an aggregated state space is learned in terms of a decision tree that groups similar states together through reinforcement learning. Without the postprocess of trimming, in tree induction, the tree encodes a control policy that can be used to control the robot by means of repeatedly improving itself. Once a variety of behaviors is learned, more elaborate behaviors can be generated by selectively organizing several behaviors using another Q-learning algorithm. The composed outputs of the organized basic behaviors on the motor level are weighted using the policy learned through Q-learning. This approach uses three diverse Q-learning algorithms to learn complex behaviors. The experimental results show that the learned complicated behaviors, organized according to individual basic behaviors by the three Q-learning algorithms on different levels, can function more adaptively in a dynamic environment. [ABSTRACT FROM PUBLISHER]

This paper proposes an algorithm, called sequence prediction via enhanced episode discovery (SPEED), to predict inhabitant activity in smart homes. SPEED is a variant of the sequence prediction algorithm. It works with the episodes of smart home events that have been extracted based on the on –off states of home appliances. An episode is a set of sequential user activities that periodically occur in smart homes. The extracted episodes are processed and arranged in a finite-order Markov model. A method based on prediction by partial matching (PPM) algorithm is applied to predict the next activity from the previous history. The result shows that SPEED achieves an 88.3% prediction accuracy, which is better than LeZi Update, Active LeZi, IPAM, and C4.5. [ABSTRACT FROM PUBLISHER]

In this paper, we present an application of machine learning to distinguish between different materials based on their surface texture. Such a system can be used for the estimation of surface friction during manipulation tasks; quality assurance in the textile, cosmetics, and harvesting industries; and other applications requiring tactile sensing. Several machine learning algorithms, such as naive Bayes, decision trees, and naive Bayes trees, have been trained to distinguish textures sensed by a biologically inspired artificial finger. The finger has randomly distributed strain gauges and polyvinylidene fluoride (PVDF) films embedded in silicone. Different textures induce different intensities of vibrations in the silicone. Consequently, textures can be distinguished by the presence of different frequencies in the signal. The data from the finger are preprocessed, and the Fourier coefficients of the sensor outputs are used to train classifiers. We show that the classifiers generalize well for unseen datasets with performance exceeding previously reported algorithms. Our classifiers can distinguish between different materials, such as carpet, flooring vinyls, tiles, sponge, wood, and polyvinyl-chloride (PVC) woven mesh with an accuracy of 95\%\pm 4\% on unseen test data. [ABSTRACT FROM AUTHOR]

This paper presents a novel touch pattern recognition algorithm for dynamic proximate interaction between a robot and a human. At first, in order to guarantee reactive responses to various touch patterns, an online touch pattern algorithm is proposed based on a Temporal Decision Tree(TDT). Second, dynamic movements of a robot in a real interaction situation usually deteriorate the confidence level of the pattern classifier. A robust method to compensate for inconsistent recognition results in the dynamic interaction is proposed by a Consistency Index(CI), which estimates consistency degrees of human touch patterns over time. The algorithms are applied to a hard-cover touch recognition module, which is being developed for recognizing the four kinds of emotional touch patterns mainly used in human-robot affective interaction. The recognition performance is evaluated in a simple game scenario environment with KaMERo (KAIST Motion Expressive Robot), which is an emotionally interactive robot platform. The results show that the proposed algorithm guarantees commercially applicable recognition performance by compensating for the misclassification inherent in the dynamic movements of a robot. [ABSTRACT FROM PUBLISHER]