Domestic and real world robotics requires continuous learning of new skills and behaviors to interact with humans. Auto-supervised learning, a compromise between supervised and completely unsupervised learning, consis...
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ISBN:
(纸本)078038914X
Domestic and real world robotics requires continuous learning of new skills and behaviors to interact with humans. Auto-supervised learning, a compromise between supervised and completely unsupervised learning, consist in relying on previous knowledge to acquire new skills. We propose here to realize auto-supervised learning by exploiting statistical regularities in the sensorimotor space of a robot. In our context, it corresponds to achieve feature selection in a bayesian programming framework. We compare several feature selection algorithms and validate them on a real robotic experiment.
One of the most important application areas of Artificial Life is the simulation of complex processes. This paper shows how to use bayesian programming to model and simulate an artificial life problem: that of a worm ...
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ISBN:
(纸本)3540260323
One of the most important application areas of Artificial Life is the simulation of complex processes. This paper shows how to use bayesian programming to model and simulate an artificial life problem: that of a worm trying to live in a world full of poison. Any model of a real phenomenon is incomplete because there will always exist unknown, hidden variables that influence the phenomenon. To solve this problem we apply a new formalism, bayesian programming. The proposed worm model has been used to train a population of worms using genetic algorithms. We will see the advantages of our method compared with a classical approach. Finally, we discuss the emergent behaviour patterns we observed in some of the worms and conclude by explaining the advantages of the applied method. It is this characteristic (the emergent behaviour) which makes Artificial Life particularly appropriate for the study and simulation of complex systems for which detailed analysis, using traditional methods, is practically non-viable.
This paper presents a cognitive model for an autonomous agent based on emotional psychology and bayesian programming. A robot with emotional responses allows us to plan behaviour in a different way than present roboti...
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ISBN:
(数字)9783540320296
ISBN:
(纸本)3540292829
This paper presents a cognitive model for an autonomous agent based on emotional psychology and bayesian programming. A robot with emotional responses allows us to plan behaviour in a different way than present robotic architectures and provides us with a method of generating a new interface for human/robot interaction. The use of emotional modules means that the emotional state of the robot can be obtained directly and, therefore, it is relatively simple to obtain a virtual face that represents these emotions. An autonomous agent could have a model of the environment to be able to interact with the real universe where it is working. It is necessary to consider that any model of a real phenomenon will be incomplete due to the existence of uncertain, unknown variables that influence the phenomenon. Two example arquitectures are proposed here. Using these architectures some experimental data, to verify the correctness of this approach, is provided.
Autonomous intelligent agents paradigm has encouraged robotic researches to take another step forward in the design of control architectures replacing modules with agents. This paper presents a logical fusion between ...
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ISBN:
(纸本)9783540886358
Autonomous intelligent agents paradigm has encouraged robotic researches to take another step forward in the design of control architectures replacing modules with agents. This paper presents a logical fusion between bayesian theory and artificial intelligent agents, showing a new intelligent bayesian agent architecture oriented towards bayesian robotics. To define this architecture we will provide a common framework for developing intelligent agent applications using bayesian theory. We will also review some of the most important bayesian agent applications and we will compare them with our model. Finally, a simple robotic application will be provided as a proof of concept of the presented architecture.
In this paper, a method of obtaining vehicle hypothesis based on laser scan data only is proposed. This is implemented on the robotic vehicle, CyCab, for navigation and mapping of the static car park environment. Lase...
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ISBN:
(纸本)3540334521
In this paper, a method of obtaining vehicle hypothesis based on laser scan data only is proposed. This is implemented on the robotic vehicle, CyCab, for navigation and mapping of the static car park environment. Laser scanner data is used to obtain hypothesis on position and orientation of vehicles with bayesian programming. Using the hypothesized vehicle poses as landmarks, CyCab performs Simultaneous Localization And Mapping (SLAM). A final map consisting of the vehicle positions in the car park is obtained.
This paper presents a fusion model of robotic behaviour based on emotional psychology. The main purpose of this model is to provide a human interface that represents the present state of the robot. This interface has ...
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ISBN:
(纸本)3540290419
This paper presents a fusion model of robotic behaviour based on emotional psychology. The main purpose of this model is to provide a human interface that represents the present state of the robot. This interface has two main advantages, firstly it can easily be understood by non-computer experts, and secondly its use is independent of language. The use of emotional modules means that the emotional state of the robot can be obtained directly and, therefore, it is relatively simple to obtain a virtual face that represents these emotions. In addition, the model proposed here, is defined as a complement to the present robotic models. Some experimental data, to verify the correctness of this approach, is provided.
In this project, we took on the task of localizing an automatic vehicle and building a map of the car park in real time. This takes place within the car park of INRIA Rhone-Alpes on the CyCab vehicle with a Sick laser...
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ISBN:
(纸本)0780389123
In this project, we took on the task of localizing an automatic vehicle and building a map of the car park in real time. This takes place within the car park of INRIA Rhone-Alpes on the CyCab vehicle with a Sick laser range scanner. Our method uses only laser scanners to retrieve the position and orientations of vehicles in the car park. With the detected vehicles as landmarks, CyCab performs a localization of itself and builds a map of the car park at the same time. Classical clustering and segmentation techniques to extract line segments from the laser scan data is applied. The key contribution of the paper is the extraction of vehicle poses from the line segments using bayesian programming. The method of FastSLAM is used in localizing CyCab and estimating the pose of vehicles in the car park. A set of hypotheses is obtained as a result. The second contribution is a method of combining the set of hypotheses together to form a final map of the car park.
This article explores an application of bayesian programming to behaviours for synthetic video games characters. We address the problem of real-time reactive selection of elementary behaviours for an agent playing a f...
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This article explores an application of bayesian programming to behaviours for synthetic video games characters. We address the problem of real-time reactive selection of elementary behaviours for an agent playing a first person shooter game. We show how bayesian programming can lead to condensed and easier formalisation of finite state machine-like behaviour selection, and lend itself to learning by imitation, in a fully transparent way for the player. (C) 2004 Published by Elsevier B.V.
This article explores an application of bayesian programming to behaviours for synthetic video games characters. We address the problem of real-time reactive selection of elementary behaviours for an agent playing a f...
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This article explores an application of bayesian programming to behaviours for synthetic video games characters. We address the problem of real-time reactive selection of elementary behaviours for an agent playing a first person shooter game. We show how bayesian programming can lead to condensed and easier formalisation of finite state machine-like behaviour selection, and lend itself to learning by imitation, in a fully transparent way for the player. (C) 2004 Published by Elsevier B.V.
Autonomous navigation of a mobile robot along a predefined trajectory is a widely studied problem in the robotics community. We propose a bayesian architecture that aims at being able to replay any sensori-motor traje...
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ISBN:
(纸本)0780382323
Autonomous navigation of a mobile robot along a predefined trajectory is a widely studied problem in the robotics community. We propose a bayesian architecture that aims at being able to replay any sensori-motor trajectory - trajectory defined as a sequence of perceptions and actions - as long as the robot starts in its neighbourhood. In order to increase robustness, we also use this bayesian framework to estimate system self-confidence while the robot is moving. This work has been validated both on a simulated robot and on a real robot: the CyCab.
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