383 research outputs found

    The Meaning of Action:a review on action recognition and mapping

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    In this paper, we analyze the different approaches taken to date within the computer vision, robotics and artificial intelligence communities for the representation, recognition, synthesis and understanding of action. We deal with action at different levels of complexity and provide the reader with the necessary related literature references. We put the literature references further into context and outline a possible interpretation of action by taking into account the different aspects of action recognition, action synthesis and task-level planning

    Parametric Human Movements:Learning, Synthesis, Recognition, and Tracking

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    Correlated space formation for human whole-body motion primitives and descriptive word labels

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    AbstractThe motion capture technology has been improved, and widely used for motion analysis and synthesis in various fields, such as robotics, animation, rehabilitation, and sports engineering. A massive amount of captured human data has already been collected. These prerecorded motion data should be reused in order to make the motion analysis and synthesis more efficient. The retrieval of a specified motion data is a fundamental technique for the reuse. Imitation learning frameworks have been developed in robotics, where motion primitive data is encoded into parameters in stochastic models or dynamical systems. We have also been making research on encoding motion primitive data into Hidden Markov Models, which are referred to as “motion symbol”, and aiming at integrating the motion symbols with language. The relations between motions and words in natural language will be versatile and powerful to provide a useful interface for reusing motion data. In this paper, we construct a space of motion symbols for human whole body movements and a space of word labels assigned to those movements. Through canonical correlation analysis, these spaces are reconstructed such that a strong correlation is formed between movements and word labels. These spaces lead to a method for searching for movement data from a query of word labels. We tested our proposed approach on captured human whole body motion data, and its validity was demonstrated. Our approach serves as a fundamental technique for extracting the necessary movements from a database and reusing them

    Learning competitive ensemble of information-constrained primitives

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    Nous voulons développer des algorithmes d'apprentissage par renforcement qui permettent à l'agent apprenant d'obtenir une décomposition structurée de son comportement. L’apprentissage par renforcement hiérarchique fournit un mécanisme permettant de le faire en modularisant explicitement la politique en deux composants: un ensemble de sous-politiques de bas niveau (ou primitives) et une politique principale de haut niveau permettant de coordonner les primitives. Alors que les primitives ne doivent se spécialiser que dans une partie de l'espace d'états, la stratégie principale doit se spécialiser dans tout l'espace d'états, car elle décide du moment d'activer les primitives. Cela introduit un ``goulot d'étranglement'' dans lequel le succès de l'agent dépend du succès de la stratégie principale, ce qui en fait un point d'échec unique. Nous proposons de supprimer cette limitation en utilisant un nouveau mécanisme selon lequel les sous-politiques peuvent décider elles-mêmes dans quelle partie de l'état elles souhaitent agir. Cette prise de décision décentralisée supprime la nécessité d’une politique principale paramétrée. Nous utilisons ce mécanisme pour former une politique composée d'un ensemble de primitives, mais ne nécessitant pas de stratégie principale pour choisir entre les primitives. Nous démontrons de manière expérimentale que cette architecture de politique améliore les politiques à la fois plates et hiérarchiques en termes de généralisation. Ce travail a été soumis à la conférence NeurIPS 2019 sous la forme d’un document intitulé Apprentissage d’un ensemble concurrentiel de primitives à contraintes d’informations. Dans le premier chapitre, j'introduis des informations de base sur l’apprentissage par renforcement, l’apprentissage par renforcement hiérarchique, les goulots d’étranglement d’information, la compositionnalité et les réseaux de modules neuronaux, et explique en quoi le travail proposé au chapitre deux est lié à ces idées. Le chapitre deux décrit l’idée de former un ensemble de primitives. Je conclus ma thèse en discutant de quelques axes de recherche futurs pour les travaux décrits au chapitre deux.We want to develop reinforcement learning algorithms that enable the learning agent to obtain a structured decomposition of its behavior. Hierarchical Reinforcement Learning provides a mechanism for doing this by explicitly modularising the policy into two components --- a set of low-level sub-policies (or primitives) and a high-level master policy to coordinate between the primitives. While the primitives have to specialize to only a part of the state space, the master policy has to specialize to the entire state space as it decides when to activate which primitives. This introduces a ``bottleneck'' where the success of the agent depends on the success of the master policy, thereby making it a single point of failure. We propose to do away with this limitation by using a new mechanism where the sub-policies can decide for themselves in which part of the state they want to act. This decentralized decision making does away with the need for a parameterized master policy. We use this mechanism to train a policy that is composed of an ensemble of primitives but one that does not require a master policy to choose between the primitives. We experimentally demonstrate that this policy architecture improves over both flat and hierarchical policies in the terms of generalization. This work is under review at the NeurIPS 2019 Conference as a paper titled Learning Competitive Ensemble of Information-Constrained Primitives. In Chapter One, I provide a background to Reinforcement Learning, Hierarchical Reinforcement Learning, Information Bottleneck, Compositionality, and Neural Module Networks and discuss how the proposed work in Chapter Two relates to these ideas. Chapter Two describes the idea of training an ensemble of primitives. I conclude the thesis by discussing some future research directions for the work described in Chapter Two

    A Neurodynamic Account of Spontaneous Behaviour

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    The current article suggests that deterministic chaos self-organized in cortical dynamics could be responsible for the generation of spontaneous action sequences. Recently, various psychological observations have suggested that humans and primates can learn to extract statistical structures hidden in perceptual sequences experienced during active environmental interactions. Although it has been suggested that such statistical structures involve chunking or compositional primitives, their neuronal implementations in brains have not yet been clarified. Therefore, to reconstruct the phenomena, synthetic neuro-robotics experiments were conducted by using a neural network model, which is characterized by a generative model with intentional states and its multiple timescales dynamics. The experimental results showed that the robot successfully learned to imitate tutored behavioral sequence patterns by extracting the underlying transition probability among primitive actions. An analysis revealed that a set of primitive action patterns was embedded in the fast dynamics part, and the chaotic dynamics of spontaneously sequencing these action primitive patterns was structured in the slow dynamics part, provided that the timescale was adequately set for each part. It was also shown that self-organization of this type of functional hierarchy ensured robust action generation by the robot in its interactions with a noisy environment. This article discusses the correspondence of the synthetic experiments with the known hierarchy of the prefrontal cortex, the supplementary motor area, and the primary motor cortex for action generation. We speculate that deterministic dynamical structures organized in the prefrontal cortex could be essential because they can account for the generation of both intentional behaviors of fixed action sequences and spontaneous behaviors of pseudo-stochastic action sequences by the same mechanism
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