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检索条件"任意字段=LAB-RS, ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems"

共 30 条记录，以下是11-20 订阅

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Recurrent neural associative learning of forward and inverse kinematics for movement generation of the redundant PA-10 robot

Recurrent neural associative learning of forward and inverse...

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ecsis symposium on learning and adaptive behaviors for robotic systems

作者： Reinhart, R. Felix Steil, Jochen J. Univ Bielefeld Res Inst Cognit & Robot CoR Lab D-4800 Bielefeld Germany

ISBN: (纸本)9780769532721

We present a connectionist approach to learn forward and redundant inverse kinematics in a single recurrent network. The network architecture extends the reservoir computing idea, i.e. to read out the state of a fixed dynamic system, into an associative setting, which learns the forward and backward mapping simultaneously. For output learning we use efficient Backpropagation-Decorrelation learning while the recurrent dynamics is adjusted by an unsupervised biologically inspired learning rule based on intrinsic plasticity. Including linear connections between input and output allows to train the network for autonomous movement generation. We show results for the 7-DOF redundant PA-10 robot arm in simulation.

关键词： Inverse kinematics

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adaptive communication promotes sub-system formation in a multi agent system with limited resources

Adaptive communication promotes sub-system formation in a mu...

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ecsis symposium on learning and adaptive behaviors for robotic systems

作者： Paolo, Di Prodi Porr, Bernd Woergoetter, Florentin Univ Glasgow Dep E&E Engn Glasgow G12 8LT Lanark Scotland Univ Gottingen Bernstein Ctr Comp Neuro Gottingen Germany

ISBN: (纸本)9780769532721

In society subsystems are formed to reduce uncertainty Subsystems are composed by agents with a reduced behavioural complexity. For example in society there are people who produce goods and other who distribute them. In this paper we show, that sub-systems emerge when the agents art able to learn and have the ability to communicate. Both the behaviour and communication is learned by the agent and is not imposed on the agent. Here the task is to collect food, keep it and eat it until sated. Every agent communicates its satedness state to neighbouring agents. This results in two subsystems whereas agents in the first collect food and in the latter steal food from others. The ratio between the number of agents that belongs to the first system and to the second system, depends on the number of food resources which are limited in space and time.

关键词： Multi agent systems

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learning vision algorithms for real mobile robots with genetic programming

Learning vision algorithms for real mobile robots with genet...

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ecsis symposium on learning and adaptive behaviors for robotic systems

作者： Barate, Renaud Manzanera, Antoine ENSTA UEI F-75015 Paris France

ISBN: (纸本)9780769532721

We present a genetic programming system to evolve vision based obstacle avoidance algorithms. In order to develop autonomous behavior in a mobile robot, our purpose is to design automatically an obstacle avoidance controller adapted to the current context. We first record short sequences where we manually guide the robot to move away from the walls. This set of recorded video images and commands is our learning base. Genetic programming is used as a supervised learning system to generate algorithms that exhibit this corridor centering behavior: We show that the generated algorithms are efficient in the corridor that was used to build the learning base, and that they generalize to some extent when the robot is placed in a visually different corridor More, the evolution process has produced algorithms that go past a limitation of our system, that is the lack of adequate edge extraction primitives. This is a good indication of the ability of this method to find efficient solutions kinds of environments.

关键词： Mobile robots

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Continuous adaptation in robotic systems by Indirect Online Evolution

Continuous adaptation in robotic systems by Indirect Online ...

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ecsis symposium on learning and adaptive behaviors for robotic systems

作者： Furuholmen, Marcus Hovin, Mats Torresen, Jim Glette, Kyrre Aker Subsea AS Snaroyveien 36POB 169 N-1364 Fornebu Norway Univ Oslo Dept Informat N-0316 Oslo Norway

ISBN: (纸本)9780769532721

A conceptual framework for online evolution in robotic systems called Indirect Online Evolution (IDOE) is presented. A model specie automatically infers models of a hidden physical system by the use of Gene Expression Programming (GEP). A parameter specie simultaneously optimizes the parameters of the inferred models according to a specified target vector. Training vectors required for modelling are automatically provided online by the interplay between the two coevolving species and the physical system. At every generation, only the estimated fittest individual of the parameter specie is executed on the physical system. This approach thus limits both the evaluation time, the wear out and the potential hazards normally associated with direct online evolution (DOE) where every individual has to be evaluated on the physical system. Additionally, the approach enables continuous system identification and adaptation during normal operation. Features of IDOE are illustrated by inferring models of a simplified, robotic arm, and further optimizing the parameters of the system according to a target position of the end effector. Simulated experiments indicate that the fitness of the IDOE approach is generally higher than the average fitness of IDOE.

关键词： End effectors

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learning robot dynamics for computed torque control using local Gaussian processes regression

Learning robot dynamics for computed torque control using lo...

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ecsis symposium on learning and adaptive behaviors for robotic systems

作者： Nguyen-Tuong, Duy Peters, Jan Max Planck Inst Biol Cybernet D-72076 Tubingen Germany

ISBN: (纸本)9780769532721

Accurate models of the robot dynamics allow the design of significantly more precise, energy-efficient and more compliant computed torque control for robots. However, in some cases the accuracy of rigid-body models does not suffice for sound control performance due to unmodeled nonlinearities such as hydraulic cables, complex friction, or actuator dynamics. In such cases, learning the models from data poses an interesting alternative and estimating the dynamics model using regression techniques becomes an important problem. However, the most accurate regression methods, e.g. Gaussian processes regression (GPR) and support vector regression (SVR), suffer from exceptional high computational complexity which prevents their usage for large numbers of samples or online learning to date. We proposed an approximation to the standard GPR using local Gaussian processes models inspired by [1], [2]. Due to reduced computational cost, local Gaussian processes (LGP) is capable for an online learning. Comparisons with other nonparametric regressions, e.g. standard GPR, v-SVR and locally weighted projection regression (LWPR), show that LGP has higher accuracy than LWPR and close to the performance of standard GPR and v-SVR while being sufficiently fast for online learning.

关键词： Gaussian noise (electronic)

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Autonomous navigation of an unmanned ground vehicle in unstructured forest terrain

Autonomous navigation of an unmanned ground vehicle in unstr...

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ecsis symposium on learning and adaptive behaviors for robotic systems

作者： Alberts, Joel Edwards, Dean Soule, Terence Anderson, Mike O'Rourke, Michael Univ Idaho Ctr Intelligent Syst Res Moscow ID 83844 USA

ISBN: (纸本)9780769532721

Autonomous vehicles con significantly improve efficiency and safety in applications ranging from warfare to transportation. However, to supply those benefits they must be shown to operate effectively, safely, and reliably in a wide range of terrains and conditions. Most major successes with autonomous vehicles have been limited to somewhat structured environments. We are interested in autonomous vehicles that can operate in forested areas, which are one of the most unstructured and difficult terrains due to the high number and varied nature of potential obstacles, the complexity of the visual field, and the difficulty in getting a good GPS fix due to overhead interference. In this paper we present a novel control system designed with the eventual goal of forest operation. It is built on top of the learning Applied to Ground robotics (LAGR) system developed at Carnegie Mellon. The new control system consists of the University of Idaho (UI) LAGR Planner and the UI software for LAGR Vision system. The results show that the combination of these two modules significantly improve the capabilities of the LAGR robot and, more importantly, allow it to perform autonomously in complex environments such as primitive forest trails that the base system could not navigate.

关键词： Control systems

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Improving robotic System Robustness via a Generalised Formal Artificial Neural System

Improving Robotic System Robustness via a Generalised Formal...

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lab-rs, ecsis symposium on learning and adaptive behaviors for robotic systems

作者： Gareth Howells Konstantinos Sirlantzis Department of Electronics University of Kent Canterbury Kent UK

ISBN: (纸本)9780769532721

A major concern for robotic guidance systems is that a temporary or permanent failure of a given sensor within the system will erroneously trigger a potential system failure state. This paper introduces a generalised artificial neural system which is capable of addressing such problems by means of the inclusion of a weight value able to incorporate a distinct failure value. This will serve to significantly improve the performance and reliability of the guidance system.

关键词： Artificial neural networks Training Temperature measurement Robot sensing systems Robots Neurons Prototypes

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Evolvability of Neuromodulated learning for Robots

Evolvability of Neuromodulated Learning for Robots

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lab-rs, ecsis symposium on learning and adaptive behaviors for robotic systems

作者： Peter Dürr Claudio Mattiussi Andrea Soltoggio Dario Floreano Ecole Polytechnique Fédérale de Lausanne Laboratory of Intelligent Systems Lausanne Switzerland University of Birmingham School of Computer Science Birmingham UK

Neuromodulation is thought to be one of the underlying principles of learning and memory in biological neural networks. Recent experiments have shown that neuroevolutionary methods benefit from neuromodulation in simple grid-world problems. In this paper we investigate the performance of a neuroevolutionary method applied to a more realistic robotic task. While confirming the favorable effect of neuromodulatory structures, our results indicate that the evolution of such architectures requires a mechanism which allows for selective modular targetting of the neuromodulatory connections.

关键词： Neurons Robots Artificial neural networks Sensors Evolution (biology) Distance measurement Robot sensing systems

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Drumming with a Humanoid Robot: Results from Human-Robot Interaction Studies

Drumming with a Humanoid Robot: Results from Human-Robot Int...

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lab-rs, ecsis symposium on learning and adaptive behaviors for robotic systems

作者： Hatice Kose-Bagci Kerstin Dautenhahn Chrystopher L. Nehaniv Adaptive Systems Research Group School of Computer Science University of Herfordshire Hatfield Hertfordshire UK

ISBN: (纸本)9780769532721

We summarise two human-robot interaction studies investigating a drumming experience with Kaspar, a humanoid child-sized robot, and human participants. Our aim is not to have Kaspar just replicate the humanpsilas drumming, but to engage in a 'social manner', i.e. in a call and response turn-taking interaction. The interactions are discussed in terms of imitation, turn-taking, and the effect of gender differences. This research is part of a project into developmental robotics with a particular emphasis of our work on gesture communication.

关键词： Games Robots Robot kinematics Lead Humanoid robots Error analysis Switches

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Scalable Biologically Inspired Neural Networks with Spike Time Based learning

Scalable Biologically Inspired Neural Networks with Spike Ti...

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lab-rs, ecsis symposium on learning and adaptive behaviors for robotic systems

作者： Lyle N. Long California Institute of Technology Pasadena CA USA Pennsylvania State University University Park PA USA

ISBN: (纸本)9780769532721

This paper describes the software and algorithmic issues involved in developing scalable large-scale biologically inspired spiking neural networks. These neural networks are useful in object recognition and signal processing tasks, but will also be useful in simulations to help understand the human brain. The software is written using object oriented programming and is very general and usable for processing a wide range of sensor data and for data fusion.

关键词： Neurons Biological neural networks Biology Biological system modeling Software Brain modeling Brain models

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