On Learning the Statistical Representation of a Task and Generalizing it to Various Contexts

This paper presents an architecture for solving generically the problem of extracting the relevant features of a given task in a programming by demonstration framework and the problem of generalizing the acquired knowledge to various contexts. We validate the architecture in a series of experiments, where a human demonstrator teaches a humanoid robot simple manipulatory tasks. Extracting the relevant features of the task is solved in a two-step process of dimensionality reduction. First, the combined joint angles and hand path motions are projected into a generic latent space, composed of a mixture of Gaussians (GMM) spreading across the spatial dimensions of the motion. Second, the temporal variation of the latent representation of the motion is encoded in a Hidden Markov Model (HMM). This two- step probabilistic encoding provides a measure of the spatio-temporal correlations across the different modalities collected by the robot, which determines a metric of imitation performance. A generalization of the demonstrated trajectories is then performed using Gaussians Mixture Regression (GMR). Finally, to generalize skills across contexts, we compute formally the trajectory that optimizes the metric, given the new context and the robot's specific body constraints

On Learning the Statistical Representation of a Task and Generalizing it to Various Contexts

This paper presents an architecture for solving generically the problem of extracting the relevant features of a given task in a programming by demonstration framework and the problem of generalizing the acquired knowledge to various contexts. We validate the architecture in a series of experiments, where a human demonstrator teaches a humanoid robot simple manipulatory tasks. Extracting the relevant features of the task is solved in a two-step process of dimensionality reduction. First, the combined joint angles and hand path motions are projected into a generic latent space, composed of a mixture of Gaussians (GMM) spreading across the spatial dimensions of the motion. Second, the temporal variation of the latent representation of the motion is encoded in a Hidden Markov Model (HMM). This two- step probabilistic encoding provides a measure of the spatio-temporal correlations across the different modalities collected by the robot, which determines a metric of imitation performance. A generalization of the demonstrated trajectories is then performed using Gaussians Mixture Regression (GMR). Finally, to generalize skills across contexts, we compute formally the trajectory that optimizes the metric, given the new context and the robot's specific body constraints

Information Extraction, Data Integration, and Uncertain Data Management: The State of The Art

Information Extraction, data Integration, and uncertain data management are different areas of research that got vast focus in the last two decades. Many researches tackled those areas of research individually. However, information extraction systems should have integrated with data integration methods to make use of the extracted information. Handling uncertainty in extraction and integration process is an important issue to enhance the quality of the data in such integrated systems. This article presents the state of the art of the mentioned areas of research and shows the common grounds and how to integrate information extraction and data integration under uncertainty management cover

Universal Grammar: Wittgenstein versus Chomsky

Daniele Moyal-Sharrock, ‘Universal Grammar: Wittgenstein versus Chomsky’ in M. A. Peters and J. Stickney, eds., A Companion to Wittgenstein on Education: Pedagogical Investigations (Singapore: Springer Verlag, 2017), ISBN: 9789811031342The motivations for the claim that language is innate are, for many, quite straightforward. The innateness of language is seen as the only way to solve the so-called 'logical problem of language acquisition': the mismatch between linguistic input and linguistic output. In this paper, I begin by unravelling several strands of the nativist argument, offering replies as I go along. I then give an outline of Wittgenstein's view of language acquisition, showing how it renders otiose problems posed by nativists like Chomsky – not least by means of Wittgenstein's own brand of grammar which, unlike Chomsky's, does not reside in the brain, but in our practices.Peer reviewe

Combining Language and Vision with a Multimodal Skip-gram Model

We extend the SKIP-GRAM model of Mikolov et al. (2013a) by taking visual information into account. Like SKIP-GRAM, our multimodal models (MMSKIP-GRAM) build vector-based word representations by learning to predict linguistic contexts in text corpora. However, for a restricted set of words, the models are also exposed to visual representations of the objects they denote (extracted from natural images), and must predict linguistic and visual features jointly. The MMSKIP-GRAM models achieve good performance on a variety of semantic benchmarks. Moreover, since they propagate visual information to all words, we use them to improve image labeling and retrieval in the zero-shot setup, where the test concepts are never seen during model training. Finally, the MMSKIP-GRAM models discover intriguing visual properties of abstract words, paving the way to realistic implementations of embodied theories of meaning.Comment: accepted at NAACL 2015, camera ready version, 11 page