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Tom 22 (2022): Zeszyt 3 (September 2022)

Tom 22 (2022): Zeszyt 2 (June 2022)

Tom 22 (2022): Zeszyt 1 (March 2022)

Tom 21 (2021): Zeszyt 4 (December 2021)

Tom 21 (2021): Zeszyt 3 (September 2021)

Tom 21 (2021): Zeszyt 2 (June 2021)

Tom 21 (2021): Zeszyt 1 (March 2021)

Tom 20 (2020): Zeszyt 6 (December 2020)
Special Zeszyt on New Developments in Scalable Computing

Tom 20 (2020): Zeszyt 5 (December 2020)
Special issue on Innovations in Intelligent Systems and Applications

Tom 20 (2020): Zeszyt 4 (November 2020)

Tom 20 (2020): Zeszyt 3 (September 2020)

Tom 20 (2020): Zeszyt 2 (June 2020)

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Tom 18 (2018): Zeszyt 5 (May 2018)
Special Thematic Zeszyt on Optimal Codes and Related Topics

Tom 18 (2018): Zeszyt 4 (November 2018)

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Tom 18 (2018): Zeszyt 2 (June 2018)

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Tom 17 (2017): Zeszyt 5 (December 2017)
Special Zeszyt With Selected Papers From The Workshop “Two Years Avitohol: Advanced High Performance Computing Applications 2017

Tom 17 (2017): Zeszyt 4 (November 2017)

Tom 17 (2017): Zeszyt 3 (September 2017)

Tom 17 (2017): Zeszyt 2 (June 2017)

Tom 17 (2017): Zeszyt 1 (March 2017)

Tom 16 (2016): Zeszyt 6 (December 2016)
Special issue with selection of extended papers from 6th International Conference on Logistic, Informatics and Service Science LISS’2016

Tom 16 (2016): Zeszyt 5 (October 2016)
Zeszyt Title: Special Zeszyt on Application of Advanced Computing and Simulation in Information Systems

Tom 16 (2016): Zeszyt 4 (December 2016)

Tom 16 (2016): Zeszyt 3 (September 2016)

Tom 16 (2016): Zeszyt 2 (June 2016)

Tom 16 (2016): Zeszyt 1 (March 2016)

Tom 15 (2015): Zeszyt 7 (December 2015)
Special Zeszyt on Information Fusion

Tom 15 (2015): Zeszyt 6 (December 2015)
Special Zeszyt on Logistics, Informatics and Service Science

Tom 15 (2015): Zeszyt 5 (April 2015)
Special Zeszyt on Control in Transportation Systems

Tom 15 (2015): Zeszyt 4 (November 2015)

Tom 15 (2015): Zeszyt 3 (September 2015)

Tom 15 (2015): Zeszyt 2 (June 2015)

Tom 15 (2015): Zeszyt 1 (March 2015)

Tom 14 (2014): Zeszyt 5 (December 2014)
Special Zeszyt

Tom 14 (2014): Zeszyt 4 (December 2014)

Tom 14 (2014): Zeszyt 3 (September 2014)

Tom 14 (2014): Zeszyt 2 (June 2014)

Tom 14 (2014): Zeszyt 1 (March 2014)

Tom 13 (2013): Zeszyt Special-Zeszyt (December 2013)

Tom 13 (2013): Zeszyt 4 (December 2013)
The publishing of the present issue (Tom 13, No 4, 2013) of the journal “Cybernetics and Information Technologies” is financially supported by FP7 project “Advanced Computing for Innovation” (ACOMIN), grant agreement 316087 of Call FP7 REGPOT-2012-2013-1.

Tom 13 (2013): Zeszyt 3 (September 2013)

Tom 13 (2013): Zeszyt 2 (June 2013)

Tom 13 (2013): Zeszyt 1 (March 2013)

Tom 12 (2012): Zeszyt 4 (December 2012)

Tom 12 (2012): Zeszyt 3 (September 2012)

Tom 12 (2012): Zeszyt 2 (June 2012)

Tom 12 (2012): Zeszyt 1 (March 2012)

Informacje o czasopiśmie
Format
Czasopismo
eISSN
1314-4081
ISSN
1311-9702
Pierwsze wydanie
13 Mar 2012
Częstotliwość wydawania
4 razy w roku
Języki
Angielski

Wyszukiwanie

Tom 12 (2012): Zeszyt 3 (September 2012)

Informacje o czasopiśmie
Format
Czasopismo
eISSN
1314-4081
ISSN
1311-9702
Pierwsze wydanie
13 Mar 2012
Częstotliwość wydawania
4 razy w roku
Języki
Angielski

Wyszukiwanie

10 Artykułów
Otwarty dostęp

Preface

Data publikacji: 22 Mar 2013
Zakres stron: 3 - 86

Abstrakt

Otwarty dostęp

Which Object Comes Next? Grounded Order Completion by a Humanoid Robot

Data publikacji: 22 Mar 2013
Zakres stron: 5 - 16

Abstrakt

Abstract

This paper describes a framework that a robot can use to complete the ordering of a set of objects. Given two sets of objects, an ordered set and an unordered set, the robot’s task is to select one object from the unordered set that best completes the ordering in the ordered set. In our experiments, the robot interacted with each object using a set of exploratory behaviors, while recording feedback from two sensory modalities (audio and proprioception). For each behavior and modality combination, the robot used the feedback sequence to estimate the perceptual distance for every pair of objects. The estimated object distance features were subsequently used to solve ordering tasks. The framework was tested on object completion tasks in which the objects varied by weight, compliance, and height. The robot was able to solve all of these tasks with a high degree of accuracy.

Słowa kluczowe

  • Developmental robotics
  • object exploration
  • grounding
Otwarty dostęp

Towards Autonomous Robotic Valve Turning

Data publikacji: 22 Mar 2013
Zakres stron: 17 - 26

Abstrakt

Abstract

In this paper an autonomous intervention robotic task to learn the skill of grasping and turning a valve is described. To resolve this challenge a set of different techniques are proposed, each one realizing a specific task and sending information to the others in a Hardware-In-Loop (HIL) simulation. To improve the estimation of the valve position, an Extended Kalman Filter is designed. Also to learn the trajectory to follow with the robotic arm, Imitation Learning approach is used. In addition, to perform safely the task a fuzzy system is developed which generates appropriate decisions. Although the achievement of this task will be used in an Autonomous Underwater Vehicle, for the first step this idea has been tested in a laboratory environment with an available robot and a sensor.

Słowa kluczowe

  • Autonomous Underwater Vehicle (AUV)
  • Imitation Learning
  • Fuzzy System
  • Extended Kalman Filter (EKF)
  • Valve Turning
Otwarty dostęp

Learning to Generalize from Demonstrations

Data publikacji: 22 Mar 2013
Zakres stron: 27 - 38

Abstrakt

Abstract

Learning by demonstration is a natural approach that can be used to build a robot’s task repertoire. In this paper we propose an algorithm that enables a learner to generalize a task representation from a small number of demonstrations of the same task. The algorithm can generalize a wide range of situations that typically occur in daily tasks. The paper also describes the supporting representation that we use in order to encode the generalized representation. The approach is validated with experimental results on a broad range of generalizations.

Słowa kluczowe

  • Learning by Demonstration
  • Generalized Representation
  • Graph Task Representation
  • Behavior Graphs
  • Robotics
Otwarty dostęp

On Global Optimization of Walking Gaits for the Compliant Humanoid Robot, COMAN Using Reinforcement Learning

Data publikacji: 22 Mar 2013
Zakres stron: 39 - 52

Abstrakt

Abstract

In ZMP trajectory generation using simple models, often a considerable amount of trials and errors are involved to obtain locally stable gaits by manually tuning the gait parameters. In this paper a 15 degrees of Freedom dynamic model of a compliant humanoid robot is used, combined with reinforcement learning to perform global search in the parameter space to produce stable gaits. It is shown that for a given speed, multiple sets of parameters, namely step sizes and lateral sways, are obtained by the learning algorithm which can lead to stable walking. The resulting set of gaits can be further studied in terms of parameter sensitivity and also to include additional optimization criteria to narrow down the chosen walking trajectories for the humanoid robot.

Słowa kluczowe

  • Humanoid robot walking
  • compliance
  • reinforcement learning
Otwarty dostęp

Combining Local and Global Direct Derivative-Free Optimization for Reinforcement Learning

Data publikacji: 22 Mar 2013
Zakres stron: 53 - 65

Abstrakt

Abstract

We consider the problem of optimization in policy space for reinforcement learning. While a plethora of methods have been applied to this problem, only a narrow category of them proved feasible in robotics. We consider the peculiar characteristics of reinforcement learning in robotics, and devise a combination of two algorithms from the literature of derivative-free optimization. The proposed combination is well suited for robotics, as it involves both off-line learning in simulation and on-line learning in the real environment. We demonstrate our approach on a real-world task, where an Autonomous Underwater Vehicle has to survey a target area under potentially unknown environment conditions. We start from a given controller, which can perform the task under foreseeable conditions, and make it adaptive to the actual environment.

Słowa kluczowe

  • Reinforcement learning
  • policy search
  • derivative-free optimization
  • robotics
  • autonomous underwater vehicles
Otwarty dostęp

Learning Fast Quadruped Robot Gaits with the RL PoWER Spline Parameterization

Data publikacji: 22 Mar 2013
Zakres stron: 66 - 75

Abstrakt

Abstract

Legged robots are uniquely privileged over their wheeled counterparts in their potential to access rugged terrain. However, designing walking gaits by hand for legged robots is a difficult and time-consuming process, so we seek algorithms for learning such gaits to automatically using real world experimentation. Numerous previous studies have examined a variety of algorithms for learning gaits, using an assortment of different robots. It is often difficult to compare the algorithmic results from one study to the next, because the conditions and robots used vary. With this in mind, we have used an open-source, 3D printed quadruped robot called QuadraTot, so the results may be verified, and hopefully improved upon, by any group so desiring. Because many robots do not have accurate simulators, we test gait-learning algorithms entirely on the physical robot. Previous studies using the QuadraTot have compared parameterized splines, the HyperNEAT generative encoding and genetic algorithm. Among these, the research on the genetic algorithm was conducted by (G l e t t e et al., 2012) in a simulator and tested on a real robot. Here we compare these results to an algorithm called Policy learning by Weighting Exploration with the Returns, or RL PoWER. We report that this algorithm has learned the fastest gait through only physical experiments yet reported in the literature, 16.3% faster than reported for HyperNEAT. In addition, the learned gaits are less taxing on the robot and more repeatable than previous record-breaking gaits.

Słowa kluczowe

  • Evolvable splines
  • parameterized gaits
  • HyperNEAT
  • machine learning
  • quadruped
Otwarty dostęp

Optimization of a Compact Model for the Compliant Humanoid Robot COMAN Using Reinforcement Learning

Data publikacji: 22 Mar 2013
Zakres stron: 76 - 85

Abstrakt

Abstract

COMAN is a compliant humanoid robot. The introduction of passive compliance in some of its joints affects the dynamics of the whole system. Unlike traditional stiff robots, there is a deflection of the joint angle with respect to the desired one whenever an external torque is applied. Following a bottom up approach, the dynamic equations of the joints are defined first. Then, a new model which combines the inverted pendulum approach with a three-dimensional (Cartesian) compliant model at the level of the center of mass is proposed. This compact model is based on some assumptions that reduce the complexity but at the same time affect the precision. To address this problem, additional parameters are inserted in the model equation and an optimization procedure is performed using reinforcement learning. The optimized model is experimentally validated on the COMAN robot using several ZMP-based walking gaits.

Słowa kluczowe

  • Humanoid robot
  • Reinforcement learning
  • Dynamic walking
Otwarty dostęp

Individual Recognition from Gait Using Feature Value Method

Data publikacji: 22 Mar 2013
Zakres stron: 86 - 95

Abstrakt

Abstract

We propose a novel framework to recognize individuals from gait, in order to improve HRI. We collected the motion data of the torso from 13 persons’ gait, using 2 IMU sensors. We developed Feature Value Method which is a PCA based classifier and we achieved an average individual recognition rate of 94% through cross-validation.

Słowa kluczowe

  • Gait
  • Recognition
  • PCA
  • Feature vector
  • Exclusion method
Otwarty dostęp

A Robotized Projective Interface for Human-Robot Learning Scenarios

Data publikacji: 22 Mar 2013
Zakres stron: 96 - 106

Abstrakt

Abstract

In this work we discuss a novel robotics interface with perception and projection capabilities for facilitating the skill transfer process. The interface aims at allowing humans and robots to interact with each other in the same environment, with respect to visual feedback. During the learning process, the real workspace can be used as a graphical interface for helping the user to better understand what the robot has learned up to then, to display information about the task or to get feedback and guidance. Thus, the user can incrementally visualize and assess the learner's state and, at the same time, focus on the skill transfer without disrupting the continuity of the teaching interaction. We also propose a proof-of-concept, as a core element of the architecture, based on an experimental setting where a picoprojector and an rgb-depth sensor are mounted onto the end-effector of a 7-DOF robotic arm.

Słowa kluczowe

  • Human-Robot Interaction
  • Learning from Demonstration
  • Augmented Reality
10 Artykułów
Otwarty dostęp

Preface

Data publikacji: 22 Mar 2013
Zakres stron: 3 - 86

Abstrakt

Otwarty dostęp

Which Object Comes Next? Grounded Order Completion by a Humanoid Robot

Data publikacji: 22 Mar 2013
Zakres stron: 5 - 16

Abstrakt

Abstract

This paper describes a framework that a robot can use to complete the ordering of a set of objects. Given two sets of objects, an ordered set and an unordered set, the robot’s task is to select one object from the unordered set that best completes the ordering in the ordered set. In our experiments, the robot interacted with each object using a set of exploratory behaviors, while recording feedback from two sensory modalities (audio and proprioception). For each behavior and modality combination, the robot used the feedback sequence to estimate the perceptual distance for every pair of objects. The estimated object distance features were subsequently used to solve ordering tasks. The framework was tested on object completion tasks in which the objects varied by weight, compliance, and height. The robot was able to solve all of these tasks with a high degree of accuracy.

Słowa kluczowe

  • Developmental robotics
  • object exploration
  • grounding
Otwarty dostęp

Towards Autonomous Robotic Valve Turning

Data publikacji: 22 Mar 2013
Zakres stron: 17 - 26

Abstrakt

Abstract

In this paper an autonomous intervention robotic task to learn the skill of grasping and turning a valve is described. To resolve this challenge a set of different techniques are proposed, each one realizing a specific task and sending information to the others in a Hardware-In-Loop (HIL) simulation. To improve the estimation of the valve position, an Extended Kalman Filter is designed. Also to learn the trajectory to follow with the robotic arm, Imitation Learning approach is used. In addition, to perform safely the task a fuzzy system is developed which generates appropriate decisions. Although the achievement of this task will be used in an Autonomous Underwater Vehicle, for the first step this idea has been tested in a laboratory environment with an available robot and a sensor.

Słowa kluczowe

  • Autonomous Underwater Vehicle (AUV)
  • Imitation Learning
  • Fuzzy System
  • Extended Kalman Filter (EKF)
  • Valve Turning
Otwarty dostęp

Learning to Generalize from Demonstrations

Data publikacji: 22 Mar 2013
Zakres stron: 27 - 38

Abstrakt

Abstract

Learning by demonstration is a natural approach that can be used to build a robot’s task repertoire. In this paper we propose an algorithm that enables a learner to generalize a task representation from a small number of demonstrations of the same task. The algorithm can generalize a wide range of situations that typically occur in daily tasks. The paper also describes the supporting representation that we use in order to encode the generalized representation. The approach is validated with experimental results on a broad range of generalizations.

Słowa kluczowe

  • Learning by Demonstration
  • Generalized Representation
  • Graph Task Representation
  • Behavior Graphs
  • Robotics
Otwarty dostęp

On Global Optimization of Walking Gaits for the Compliant Humanoid Robot, COMAN Using Reinforcement Learning

Data publikacji: 22 Mar 2013
Zakres stron: 39 - 52

Abstrakt

Abstract

In ZMP trajectory generation using simple models, often a considerable amount of trials and errors are involved to obtain locally stable gaits by manually tuning the gait parameters. In this paper a 15 degrees of Freedom dynamic model of a compliant humanoid robot is used, combined with reinforcement learning to perform global search in the parameter space to produce stable gaits. It is shown that for a given speed, multiple sets of parameters, namely step sizes and lateral sways, are obtained by the learning algorithm which can lead to stable walking. The resulting set of gaits can be further studied in terms of parameter sensitivity and also to include additional optimization criteria to narrow down the chosen walking trajectories for the humanoid robot.

Słowa kluczowe

  • Humanoid robot walking
  • compliance
  • reinforcement learning
Otwarty dostęp

Combining Local and Global Direct Derivative-Free Optimization for Reinforcement Learning

Data publikacji: 22 Mar 2013
Zakres stron: 53 - 65

Abstrakt

Abstract

We consider the problem of optimization in policy space for reinforcement learning. While a plethora of methods have been applied to this problem, only a narrow category of them proved feasible in robotics. We consider the peculiar characteristics of reinforcement learning in robotics, and devise a combination of two algorithms from the literature of derivative-free optimization. The proposed combination is well suited for robotics, as it involves both off-line learning in simulation and on-line learning in the real environment. We demonstrate our approach on a real-world task, where an Autonomous Underwater Vehicle has to survey a target area under potentially unknown environment conditions. We start from a given controller, which can perform the task under foreseeable conditions, and make it adaptive to the actual environment.

Słowa kluczowe

  • Reinforcement learning
  • policy search
  • derivative-free optimization
  • robotics
  • autonomous underwater vehicles
Otwarty dostęp

Learning Fast Quadruped Robot Gaits with the RL PoWER Spline Parameterization

Data publikacji: 22 Mar 2013
Zakres stron: 66 - 75

Abstrakt

Abstract

Legged robots are uniquely privileged over their wheeled counterparts in their potential to access rugged terrain. However, designing walking gaits by hand for legged robots is a difficult and time-consuming process, so we seek algorithms for learning such gaits to automatically using real world experimentation. Numerous previous studies have examined a variety of algorithms for learning gaits, using an assortment of different robots. It is often difficult to compare the algorithmic results from one study to the next, because the conditions and robots used vary. With this in mind, we have used an open-source, 3D printed quadruped robot called QuadraTot, so the results may be verified, and hopefully improved upon, by any group so desiring. Because many robots do not have accurate simulators, we test gait-learning algorithms entirely on the physical robot. Previous studies using the QuadraTot have compared parameterized splines, the HyperNEAT generative encoding and genetic algorithm. Among these, the research on the genetic algorithm was conducted by (G l e t t e et al., 2012) in a simulator and tested on a real robot. Here we compare these results to an algorithm called Policy learning by Weighting Exploration with the Returns, or RL PoWER. We report that this algorithm has learned the fastest gait through only physical experiments yet reported in the literature, 16.3% faster than reported for HyperNEAT. In addition, the learned gaits are less taxing on the robot and more repeatable than previous record-breaking gaits.

Słowa kluczowe

  • Evolvable splines
  • parameterized gaits
  • HyperNEAT
  • machine learning
  • quadruped
Otwarty dostęp

Optimization of a Compact Model for the Compliant Humanoid Robot COMAN Using Reinforcement Learning

Data publikacji: 22 Mar 2013
Zakres stron: 76 - 85

Abstrakt

Abstract

COMAN is a compliant humanoid robot. The introduction of passive compliance in some of its joints affects the dynamics of the whole system. Unlike traditional stiff robots, there is a deflection of the joint angle with respect to the desired one whenever an external torque is applied. Following a bottom up approach, the dynamic equations of the joints are defined first. Then, a new model which combines the inverted pendulum approach with a three-dimensional (Cartesian) compliant model at the level of the center of mass is proposed. This compact model is based on some assumptions that reduce the complexity but at the same time affect the precision. To address this problem, additional parameters are inserted in the model equation and an optimization procedure is performed using reinforcement learning. The optimized model is experimentally validated on the COMAN robot using several ZMP-based walking gaits.

Słowa kluczowe

  • Humanoid robot
  • Reinforcement learning
  • Dynamic walking
Otwarty dostęp

Individual Recognition from Gait Using Feature Value Method

Data publikacji: 22 Mar 2013
Zakres stron: 86 - 95

Abstrakt

Abstract

We propose a novel framework to recognize individuals from gait, in order to improve HRI. We collected the motion data of the torso from 13 persons’ gait, using 2 IMU sensors. We developed Feature Value Method which is a PCA based classifier and we achieved an average individual recognition rate of 94% through cross-validation.

Słowa kluczowe

  • Gait
  • Recognition
  • PCA
  • Feature vector
  • Exclusion method
Otwarty dostęp

A Robotized Projective Interface for Human-Robot Learning Scenarios

Data publikacji: 22 Mar 2013
Zakres stron: 96 - 106

Abstrakt

Abstract

In this work we discuss a novel robotics interface with perception and projection capabilities for facilitating the skill transfer process. The interface aims at allowing humans and robots to interact with each other in the same environment, with respect to visual feedback. During the learning process, the real workspace can be used as a graphical interface for helping the user to better understand what the robot has learned up to then, to display information about the task or to get feedback and guidance. Thus, the user can incrementally visualize and assess the learner's state and, at the same time, focus on the skill transfer without disrupting the continuity of the teaching interaction. We also propose a proof-of-concept, as a core element of the architecture, based on an experimental setting where a picoprojector and an rgb-depth sensor are mounted onto the end-effector of a 7-DOF robotic arm.

Słowa kluczowe

  • Human-Robot Interaction
  • Learning from Demonstration
  • Augmented Reality

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