Search

We propose a novel online learning algorithm, called SpCoSLAM 2.0, for spatial concepts and lexical acquisition with high accuracy and scalability. Previously, we proposed SpCoSLAM as an online learning algorithm based on unsupervised Bayesian probabilistic model that integrates multimodal place categorization, lexical acquisition, and SLAM. However, our original algorithm had limited estimation accuracy owing to the influence of the early stages of learning, and increased computational complexity with added training data. Therefore, we introduce techniques such as fixed-lag rejuvenation to reduce the calculation time while maintaining an accuracy higher than that of the original algorithm. The results show that, in terms of estimation accuracy, the proposed algorithm exceeds the original algorithm and is comparable to batch learning. In addition, the calculation time of the proposed algorithm does not depend on the amount of training data and becomes constant for each step of the scalable algorithm. Our approach will contribute to the realization of long-term spatial language interactions between humans and robots. [ABSTRACT FROM AUTHOR]

Keywords: machine leading; cognitive robotics; language acquisition; neural networks; cognitive architecture; probabilistic models; robot learning Following the successfully organized session "the Workshop on Machine Learning Methods for High-Level Cognitive Capabilities in Robotics 2016" held in IEEE-IROS 2016[1], we organized this Research Topic. Nakajo et al. proposed a machine learning method for viewpoint transformation and action mapping using a neural network having encoder-decoder architecture, i.e., sequence to sequence. Machine leading, cognitive robotics, language acquisition, neural networks, cognitive architecture, probabilistic models, robot learning. [Extracted from the article]

This paper describes how to achieve highly accurate unsupervised spatial lexical acquisition from speech-recognition results including phoneme recognition errors. In most research into lexical acquisition, the robot has no pre-existing lexical knowledge. The robot acquires sequences of some phonemes as words from continuous speech signals. In a previous study, we proposed a nonparametric Bayesian spatial concept acquisition method (SpCoA) that integrates the robot’s position and words obtained by unsupervised word segmentation from uncertain syllable recognition results. However, SpCoA has a very critical problem to be solved in lexical acquisition; the boundaries of word segmentation are incorrect in many cases because of many phoneme recognition errors. Therefore, we propose an unsupervised machine learning method (SpCoA++) for the robust lexical acquisition of novel words relating to places visited by the robot. The proposed SpCoA++ method performs an iterative estimation of learning spatial concepts and updating a language model using place information. SpCoA++ can select a candidate including many words that better represent places from multiple word-segmentation results by maximizing the mutual information between segmented words and spatial concepts. The experimental results demonstrate a significant improvement of the phoneme accuracy rate of learned words relating to place in the proposed method by word-segmentation results based on place information, in comparison to the conventional methods. We indicate that the proposed method enables the robot to acquire words from speech signals more accurately, and improves the estimation accuracy of the spatial concepts. [ABSTRACT FROM AUTHOR]

In this paper, we propose a visualization method for driving behavior that helps people to recognize distinctive driving behavior patterns in continuous driving behavior data. Driving behavior can be measured using various types of sensors connected to a control area network. The measured multi-dimensional time series data are called driving behavior data. In many cases, each dimension of the time series data is not independent of each other in a statistical sense. For example, accelerator opening rate and longitudinal acceleration are mutually dependent. We hypothesize that only a small number of hidden features that are essential for driving behavior are generating the multivariate driving behavior data. Thus, extracting essential hidden features from measured redundant driving behavior data is a problem to be solved to develop an effective visualization method for driving behavior. In this paper, we propose using deep sparse autoencoder (DSAE) to extract hidden features for visualization of driving behavior. Based on the DSAE, we propose a visualization method called a driving color map by mapping the extracted 3-D hidden feature to the red green blue (RGB) color space. A driving color map is produced by placing the colors in the corresponding positions on the map. The subjective experiment shows that feature extraction method based on the DSAE is effective for visualization. In addition, its performance is also evaluated numerically by using pattern recognition method. We also provide examples of applications that use driving color maps in practical problems. In summary, it is shown the driving color map based on DSAE facilitates better visualization of driving behavior. [ABSTRACT FROM PUBLISHER]

Innovations in artificial intelligence have opened the door to the next generation of cognitive robotics. This special issue aims to not only report the latest research results in these fields, but also to further discuss the future cognitive robotics in the 2020s and beyond. We are pleased to announce the special issue on Symbol Emergence in Robotics and Cognitive Systems. [Extracted from the article]

A sequence prediction method for driving behavior data is proposed in this paper. The proposed method can predict a longer latent state sequence of driving behavior data than conventional sequence prediction methods. The proposed method is derived by focusing on the double articulation structure latently embedded in driving behavior data. The double articulation structure is a two-layer hierarchical structure originally found in spoken language, i.e., a sentence is a sequence of words and a word is a sequence of letters. Analogously, we assume that driving behavior data comprise a sequence of driving words and a driving word is a sequence of driving letters. The sequence prediction method is obtained by extending a nonparametric Bayesian unsupervised morphological analyzer using a nested Pitman-Yor language model (NPYLM), which was originally proposed in the natural language processing field. This extension allows the proposed method to analyze incomplete sequences of latent states of driving behavior and to predict subsequent latent states on the basis of a maximum a posteriori criterion. The extension requires a marginalization technique over an infinite number of possible driving words. We derived such a technique on the basis of several characteristics of the NPYLM. We evaluated this proposed sequence prediction method using three types of data: 1) synthetic data; 2) data from test drives around a driving course at a factory; and 3) data from drives on a public thoroughfare. The results showed that the proposed method made better long-term predictions than did the previous methods. [ABSTRACT FROM PUBLISHER]

Direct word discovery from audio speech signals is a very difficult and challenging problem for a developmental robot. Human infants are able to discover words directly from speech signals, and, to understand human infants' developmental capability using a constructive approach, it is very important to build a machine learning system that can acquire knowledge about words and phonemes, i.e. a language model and an acoustic model, autonomously in an unsupervised manner. To achieve this, the nonparametric Bayesian double articulation analyzer (NPB-DAA) with the deep sparse autoencoder (DSAE) is proposed in this paper. The NPB-DAA has been proposed to achieve totally unsupervised direct word discovery from speech signals. However, the performance was still unsatisfactory, although it outperformed pre-existing unsupervised learning methods. In this paper, we integrate the NPB-DAA with the DSAE, which is a neural network model that can be trained in an unsupervised manner, and demonstrate its performance through an experiment about direct word discovery from auditory speech signals. The experiment shows that the combinedmethod, the NPB-DAAwith the DSAE, outperforms pre-existing unsupervised learningmethods, and shows stateof- the-art performance. It is also shown that the proposed method outperforms several standard speech recognizer-based methods with true word dictionaries. [ABSTRACT FROM AUTHOR]

In-vehicle speech-based interaction between a driver and a driving agent should be performed without affecting the driving behavior. A driving agent provides information to the driver and helps his/her driving behavior and non-driving-related tasks, e.g., selecting music and giving weather information. In this paper, we focus on a method for determining utterance timings when a driving agent provides non-driving-related information. If a driving agent provides a driver with non-driving-related information at an inappropriate moment, it will distract his/her driving behavior and deteriorate his/her safety driving. To solve or to mitigate the problem, we propose a novel method for determining the utterance timing of a driving agent on the basis of a double articulation analyzer, which is an unsupervised nonparametric Bayesian machine learning method for detecting contextual change points. To verify the effectiveness of the method, we conduct two experiments. One is an experiment on a short circuit around a park in an urban area, and the other is an experiment on a long course in a town. The results show that the proposed method enables a driving agent to avoid inappropriate timing better than baseline methods. [ABSTRACT FROM PUBLISHER]

An unsupervised learning method, called double articulation analyzer with temporal prediction (DAA-TP), is proposed on the basis of the original DAA model. The method will enable future advanced driving assistance systems to determine driving context and predict possible scenarios of driving behavior by segmenting and modeling incoming driving-behavior time series data. In previous studies, we applied the DAA model to driving-behavior data and argued that contextual changing points can be estimated as changing points of chunks. A sequence prediction method, which predicts the next hidden state sequence, was also proposed in a previous study. However, the original DAA model does not model the duration of chunks of driving behavior and is not able to do a temporal prediction of the scenarios. Our DAA-TP method explicitly models the duration of chunks of driving behavior on the assumption that driving-behavior data have a two-layered hierarchical structure, i.e., double articulation structure. For this purpose, the hierarchical Dirichlet process hidden semi-Markov model is used for explicitly modeling the duration of segments of driving-behavior data. A Poisson distribution is also used to model the duration distribution of driving-behavior segments. The duration distribution of chunks of driving-behavior data is also theoretically calculated using the reproductive property of the Poisson distribution. We also propose a calculation method for obtaining the probability distribution of the remaining duration of current driving words as a mixture of Poisson distribution with a theoretical approximation for unobserved driving words. This method can calculate the posterior probability distribution of the next termination time of chunks by explicitly modeling all probable chunking results for observed data. The DAA-TP was applied to a synthetic data set having a double articulation structure to evaluate its model consistency. To evaluate the effectiveness of DAA-TP, we applied it to a driving-behavior data set recorded at actual factory circuits. The DAA-TP could predict the next termination time of chunks more accurately than the compared methods. We also report the qualitative results for understanding the potential capability of DAA-TP. [ABSTRACT FROM PUBLISHER]

We propose an unsupervised motion segmentation method for enabling a robot to imitate and perform various unit motions by observing unsegmented human motion. Natural unsegmented human motion data contain various types of unit motions, such as 'waving good-bye', 'walking' and 'throwing a ball'. A robot has to segment the data and extract unit motions from the data to imitate the motions. In previous work, an ergodic hidden Markov model (HMM) was used to model unsegmented human motion. However, there are two main problems with the classical use of this model. (i) Setting an appropriate number of hidden states is difficult because how complex the motions contained in the learning data are and how many there are unknown. (ii) We did not have an effective chunking method that could chunk elemental motions into meaningful unit motions without being captured by local minima. To overcome these problems, we developed an unsupervised motion segmentation method for imitation learning using a sticky hierarchical Dirichlet process (HDP)-HMM, a nonparametric Bayesian model, and an unsupervised chunking method based on a Gibbs sampler and the minimal description length (MDL) principle of imitation learning of unsegmented human motion. We developed this chunking method to work with the sticky HDP-HMM and extract unit human motions. We conducted several experiments to evaluate this method. The proposed method could extract unit motions from unsegmented human motion data. The sticky HDP-HMM can be used to model unsegmented human motion more accurately than with a conventional HMM and simultaneously estimate the number of hidden states. We also evaluated the dependency of the HDP-HMM on the hyperparameters of the model. [ABSTRACT FROM AUTHOR]

A novel integrative learning architecture based on a reinforcement learning schemata model (RLSM) with a spike timing-dependent plasticity (STDP) network is described. This architecture models operant conditioning with discriminative stimuli in an autonomous agent engaged in multiple reinforcement learning tasks. The architecture consists of two constitutional learning architectures: RLSM and STDP. RLSM is an incremental modular reinforcement learning architecture, and it makes an autonomous agent acquire several behavioral concepts incrementally through continuous interactions with its environment and/or caregivers. STDP is a learning rule of neuronal plasticity found in cerebral cortices and the hippocampus of the human brain. STDP is a temporally asymmetric learning rule that contrasts with the Hebbian learning rule. We found that STDP enabled an autonomous robot to associate auditory input with its acquired behaviors and to select reinforcement learning modules more effectively. Auditory signals interpreted based on the acquired behaviors were revealed to correspond to 'signs' of required behaviors and incoming situations. This integrative learning architecture was evaluated in the context of on-line modular learning. [ABSTRACT FROM AUTHOR]