There have been several attempts at modeling context in robots. However, either these attempts assume a fixed number of contexts or use a rule-based approach to determine when to increment the number of contexts. In this paper, we pose the task of when to increment as a learning problem, which we solve using a Recurrent Neural Network. We show that the network successfully (with 98 testing accuracy) learns to predict when to increment, and demonstrate, in a scene modeling problem (where the correct number of contexts is not known), that the robot increments the number of contexts in an expected manner (i.e., the entropy of the system is reduced). We also present how the incremental model can be used for various scene reasoning tasks.
There have been some attempts at incremental context or topic modeling, in text modeling @cite_22 , computer vision @cite_19 and in robotics @cite_3 @cite_8 . However, these attempts are generally rule-based approaches, looking at the errors or the entropy (perplexity) of the system to determine when to increment. Since these rules are hand-crafted by intuition, they may fail to capture the underlying structure of the data for determining when to increment. There are also methods such as Hierarchical Dirichlet Processes @cite_10 or its nested version @cite_18 that assume the availability of all data to estimate the number of topics or assume an infinite number of topics, which are both unrealistic for robots continually interacting with the environment and getting into new contexts through their lifetime.
@cite_18 @cite_22 @cite_8 @cite_3 @cite_19 @cite_10
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We develop a nested hierarchical Dirichlet process (nHDP) for hierarchical topic modeling. The nHDP generalizes the nested Chinese restaurant process (nCRP) to allow each word to follow its own path to a topic node according to a per-document distribution over the paths on a shared tree. This alleviates the rigid, single-path formulation assumed by the nCRP, allowing documents to easily express complex thematic borrowings. We derive a stochastic variational inference algorithm for the model, which enables efficient inference for massive collections of text documents. We demonstrate our algorithm on 1.8 million documents from The New York Times and 2.7 million documents from Wikipedia . Inference algorithms for topic models are typically designed to be run over an entire collection of documents after they have been observed. However, in many applications of these models, the collection grows over time, making it infeasible to run batch algorithms repeatedly. This problem can be addressed by using online algorithms, which update estimates of the topics as each document is observed. We introduce two related RaoBlackwellized online inference algorithms for the latent Dirichlet allocation (LDA) model – incremental Gibbs samplers and particle filters – and compare their runtime and performance to that of existing algorithms. In this paper, we formalize and model context in terms of a set of concepts grounded in the sensorimotor interactions of a robot. The concepts are modeled as a web using Markov Random Field (MRF), inspired from the concept web hypothesis for representing concepts in humans. On this concept web, we treat context as a latent variable of Latent Dirichlet Allocation (LDA), which is a widely-used method in computational linguistics for modeling topics in texts. We extend the standard LDA method in order to make it incremental so that: 1) it does not relearn everything from scratch given new interactions (i.e., it is online); and 2) it can discover and add a new context into its model when necessary. We demonstrate on the iCub platform that, partly owing to modeling context on top of the concept web, our approach is adaptive, online, and robust: it is adaptive and online since it can learn and discover a new context from new interactions. It is robust since it is not affected by irrelevant stimuli and it can discover contexts after a few interactions only. Moreover, we show how to use the context learned in such a model for two important tasks: object recognition and planning. In the context of developmental robotics, a robot has to cope with complex sensorimotor spaces by reducing their dimensionality. In the case of sensor space reduction, classical approaches for pattern recognition use either hard- coded feature detection or supervised learning. We believe supervised learning and hard-coded feature extraction must be extended with unsupervised learning of feature representations. In this paper, we present an approach to learn represen- tations using space-variant images and saccades. The saccades are driven by a measure of quantity of information in the visual scene, emerging from the activations of Restricted Boltzmann Machines (RBMs). The RBM, a generative model, is trained incrementally on locations where the system saccades. Our approach is implemented using real data captured by a NAO robot in indoor conditions. I. INTRODUCTION We consider problems involving groups of data where each observation within a group is a draw from a mixture model and where it is desirable to share mixture components between groups. We assume that the number of mixture components is unknown a priori and is to be inferred from the data. In this setting it is natural to consider sets of Dirichlet processes, one for each group, where the well-known clustering property of the Dirichlet process provides a nonparametric prior for the number of mixture components within each group. Given our desire to tie the mixture models in the various groups, we consider a hierarchical model, specifically one in which the base measure for the child Dirichlet processes is itself distributed according to a Dirichlet process. Such a base measure being discrete, the child Dirichlet processes necessarily share atoms. Thus, as desired, the mixture models in the different groups necessarily share mixture components. We discuss representations of hierarchical Dirichlet processes ...