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共 37 条记录，以下是21-30 订阅

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Evolvability of Neuromodulated learning for Robots

Evolvability of Neuromodulated Learning for Robots

引用

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...

引用

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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Surprise-Based learning for Developmental robotics

Surprise-Based Learning for Developmental Robotics

引用

LAB-RS, ecsis symposium on learning and adaptive behaviors for robotic systems

作者： Nadeesha Ranasinghe Wei-Min Shen Information Sciences Institute University of Southern California CA USA

ISBN: (纸本)9780769532721

This paper presents a learning algorithm called surprise-based learning (SBL) capable of providing a physical robot the ability to autonomously learn and plan in an unknown environment without any prior knowledge of its actions or their impact on the environment. This is achieved by creating a model of the environment using prediction rules. A prediction rule describes the observations of the environment prior to the execution of an action and the forecasted or predicted observation of the environment after the action. The algorithm learns by investigating "surprises", which are inconsistencies between the predictions and observed outcome. SBL has been successfully demonstrated on a modular robot learning and navigating in a small static environment.

关键词： Robots Sensors Robot sensing systems Feature extraction Prediction algorithms Predictive models Planning

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Scalable Biologically Inspired Neural Networks with Spike Time Based learning

Scalable Biologically Inspired Neural Networks with Spike Ti...

引用

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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What is an Appropriate Theory of Imitation for a Robot Learner?

What is an Appropriate Theory of Imitation for a Robot Learn...

引用

LAB-RS, ecsis symposium on learning and adaptive behaviors for robotic systems

作者： Joe Saunders Chrystopher L. Nehaniv Kerstin Dautenhahn Adapative Systems Research Group University of Herfordshire Hatfield UK

ISBN: (纸本)9780769532721

When faced with learning new skills or trying to solve problems humans sometimes have a choice, either learn the skill oneself (individual learning) or alternatively learn the skill from someone who already has that knowledge (social learning). learning from another has the advantage of bypassing a long and costly search process and can therefore speed up learning by exploiting the knowledge of the other. It may also be the case that new and original solutions to problems may never be found when searching for them individually whereas readily available solutions may be found by exploiting the knowledge of another. Imitation is one mechanism which allows this knowledge transfer to take place. In this paper comparisons of selected theories underlying imitation and how these may be useful from a robotics viewpoint and especially a robot learner are presented. Many of these theories underlie existing robotic implementations and thus to understand the efficacy of these robot learners an understanding of the underlying concepts may prove useful and necessary.

关键词： Robots Mirrors Robot sensing systems Neurons Tongue Proposals Synchronous motors

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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...

引用

LAB-RS, ecsis symposium on learning and adaptive behaviors for robotic systems

作者： R. Felix Reinhart Jochen J. Steil Research Institute for Cognition and Robotics Bielefeld University Research Institute for Cognition and Robotics Bielefeld University 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： Reservoirs Neurons Kinematics Robots Joints Training Biological system modeling

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A Constraint-Based Behavior Fusion Mechanism on Mobile Manipulator

A Constraint-Based Behavior Fusion Mechanism on Mobile Manip...

引用

LAB-RS, ecsis symposium on learning and adaptive behaviors for robotic systems

作者： Shu Huang Erwin Aertbeliën Hendrik Van Brussel PMA Katholieke Universiteit Leuven Heverlee Belgium

ISBN: (纸本)9780769532721

A constraint-based behavior fusion mechanism is proposed. The behavior-based concept is suitable for robots in a dynamic changing environment. However, this concept suffers from two major restrictions: re-usability of the behaviors and deliberation between the behaviors. Because of this, complex tasks are still challenging for behavior-based systems. In the control of sensor-based robots, the constraint-based task specification method provides a flexible representation for a task. In this paper, these constraint equations are used as a common interface for behavior fusion. Due to this fusion ability, the intelligent behaviors concentrate on performing their own goals without worrying about the coordination with other behaviors. Thus, the complexity of the deliberate network can be reduced and becomes easier to be learned. To demonstrate this fusion mechanism, a door opening example is implemented on a mobile manipulator.

关键词： Mobile communication Manipulators Joints Robots Robot sensing systems Robot kinematics Equations

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Towards Object Classification Using 3D Sensor Data

Towards Object Classification Using 3D Sensor Data

引用

LAB-RS, ecsis symposium on learning and adaptive behaviors for robotic systems

作者： Sören Schwertfeger Jann Poppinga Max Pfingsthorn Andreas Birk School of Electrical Engineering and Computer Science Jacobs University Bremen Bremen Germany

ISBN: (纸本)9780769532721

This paper presents an approach to classify objects using 3D sensor data and an evolutionary algorithm. An important by-product of this classification is, that additionally certain properties and the pose in space of this object are determined. The reproductive perception paradigm is used utilizing an evolutionary strategy. Two sub-approaches are discussed using different representations of the 3D data. The first one uses depth images while the second one uses point clouds stored in a special octree. The approaches will be demonstrated in experiments with simulated and real data.

关键词： Three dimensional displays Classification algorithms Evolution (biology) Robot sensing systems Distance measurement Data models Arrays

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Continuous Adaptation in robotic systems by Indirect Online Evolution

Continuous Adaptation in Robotic Systems by Indirect Online ...

引用

LAB-RS, ecsis symposium on learning and adaptive behaviors for robotic systems

作者： Marcus Furuholmen Mats Hovin Jim Torresen Kyrre Glette Department of Informatics University of Oslo 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 DOE.

关键词： Training US Department of Energy Mathematical model Robots Evolution (biology) Adaptation model Data models

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learning Vision Algorithms for Real Mobile Robots with Genetic Programming

Learning Vision Algorithms for Real Mobile Robots with Genet...

引用

LAB-RS, ecsis symposium on learning and adaptive behaviors for robotic systems

作者： Renaud Barate Antoine Manzanera UEI ENSTA 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 for different kinds of environments.

关键词： Optical filters Robots Gabor filters Filtering algorithms Evolution (biology) Genetic programming Image motion analysis

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