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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21-30 订阅

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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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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...

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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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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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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...

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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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Using Cognitive Semantics to Integrate Perception and Motion in a Behavior-Based Robot

Using Cognitive Semantics to Integrate Perception and Motion...

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

作者： D. Paul Benjamin Deryle Lonsdale Damian Lyons Siddtharth Patel Pace University NY USA Department Linguistics & English Language Brigham Young University Provo UT USA Fordham University Bronx NY USA

ISBN: (纸本)9780769532721

The ADAPT project is a collaboration of three universities building a unified architecture for mobile robots. The goal of this project is to endow robots with the full range of cognitive abilities, including perception, use of natural language, learning and the ability to solve complex problems. The perspective of this work is that such an architecture should be based on language and visualization. ADAPT is based on an the structure and semantics of language, and more specifically on algebraic linguistics and visualization of semantics. ADAPT organizes its knowledge using linguistic robot schemas, which implement linguistic units within a concurrent, distributed programming language. Each schema is associated with one or more 3D visualizations that provide its semantics. These visualizations are dynamic, and are composed within a virtual world to create ADAPTpsilas representation of itself and its environment.

关键词： Robots Adaptation model Visualization Robot sensing systems Cognition Engines Natural languages

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Autonomous Navigation of an Unmanned Ground Vehicle in Unstructured Forest Terrain

Autonomous Navigation of an Unmanned Ground Vehicle in Unstr...

引用

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

作者： Joel Alberts Dean Edwards Terence Soule Mike Anderson Michael O'Rourke Center for Intelligent Systems Research University of Idaho Moscow USA Center for Intelligent Systems Research University of Idaho Moscow

ISBN: (纸本)9780769532721

Autonomous vehicles can 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： Robots Navigation Global positioning system Mobile robots Vehicles Robot kinematics Planning

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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...

引用

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

作者： Duy Nguyen-Tuong Jan Peters Max-Planck Institute of Biological Cybernetics 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 [Vijayakumaf, 2005 and Snelson and Ghahramani, 2007]. 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： Computational modeling Ground penetrating radar Data models Robots Predictive models Mathematical model Distance measurement

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