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Volumen 13 (2013): Heft Special-Heft (December 2013)

Volumen 13 (2013): Heft 4 (December 2013)
The publishing of the present issue (Volumen 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.

Volumen 13 (2013): Heft 3 (September 2013)

Volumen 13 (2013): Heft 2 (June 2013)

Volumen 13 (2013): Heft 1 (March 2013)

Volumen 12 (2012): Heft 4 (December 2012)

Volumen 12 (2012): Heft 3 (September 2012)

Volumen 12 (2012): Heft 2 (June 2012)

Volumen 12 (2012): Heft 1 (March 2012)

Zeitschriftendaten
Format
Zeitschrift
eISSN
1314-4081
ISSN
1311-9702
Erstveröffentlichung
13 Mar 2012
Erscheinungsweise
4 Hefte pro Jahr
Sprachen
Englisch

Suche

Volumen 12 (2012): Heft 3 (September 2012)

Zeitschriftendaten
Format
Zeitschrift
eISSN
1314-4081
ISSN
1311-9702
Erstveröffentlichung
13 Mar 2012
Erscheinungsweise
4 Hefte pro Jahr
Sprachen
Englisch

Suche

10 Artikel
Uneingeschränkter Zugang

Preface

Online veröffentlicht: 22 Mar 2013
Seitenbereich: 3 - 86

Zusammenfassung

Uneingeschränkter Zugang

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

Online veröffentlicht: 22 Mar 2013
Seitenbereich: 5 - 16

Zusammenfassung

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.

Schlüsselwörter

  • Developmental robotics
  • object exploration
  • grounding
Uneingeschränkter Zugang

Towards Autonomous Robotic Valve Turning

Online veröffentlicht: 22 Mar 2013
Seitenbereich: 17 - 26

Zusammenfassung

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.

Schlüsselwörter

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

Learning to Generalize from Demonstrations

Online veröffentlicht: 22 Mar 2013
Seitenbereich: 27 - 38

Zusammenfassung

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.

Schlüsselwörter

  • Learning by Demonstration
  • Generalized Representation
  • Graph Task Representation
  • Behavior Graphs
  • Robotics
Uneingeschränkter Zugang

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

Online veröffentlicht: 22 Mar 2013
Seitenbereich: 39 - 52

Zusammenfassung

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.

Schlüsselwörter

  • Humanoid robot walking
  • compliance
  • reinforcement learning
Uneingeschränkter Zugang

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

Online veröffentlicht: 22 Mar 2013
Seitenbereich: 53 - 65

Zusammenfassung

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.

Schlüsselwörter

  • Reinforcement learning
  • policy search
  • derivative-free optimization
  • robotics
  • autonomous underwater vehicles
Uneingeschränkter Zugang

Learning Fast Quadruped Robot Gaits with the RL PoWER Spline Parameterization

Online veröffentlicht: 22 Mar 2013
Seitenbereich: 66 - 75

Zusammenfassung

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.

Schlüsselwörter

  • Evolvable splines
  • parameterized gaits
  • HyperNEAT
  • machine learning
  • quadruped
Uneingeschränkter Zugang

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

Online veröffentlicht: 22 Mar 2013
Seitenbereich: 76 - 85

Zusammenfassung

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.

Schlüsselwörter

  • Humanoid robot
  • Reinforcement learning
  • Dynamic walking
Uneingeschränkter Zugang

Individual Recognition from Gait Using Feature Value Method

Online veröffentlicht: 22 Mar 2013
Seitenbereich: 86 - 95

Zusammenfassung

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.

Schlüsselwörter

  • Gait
  • Recognition
  • PCA
  • Feature vector
  • Exclusion method
Uneingeschränkter Zugang

A Robotized Projective Interface for Human-Robot Learning Scenarios

Online veröffentlicht: 22 Mar 2013
Seitenbereich: 96 - 106

Zusammenfassung

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.

Schlüsselwörter

  • Human-Robot Interaction
  • Learning from Demonstration
  • Augmented Reality
10 Artikel
Uneingeschränkter Zugang

Preface

Online veröffentlicht: 22 Mar 2013
Seitenbereich: 3 - 86

Zusammenfassung

Uneingeschränkter Zugang

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

Online veröffentlicht: 22 Mar 2013
Seitenbereich: 5 - 16

Zusammenfassung

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.

Schlüsselwörter

  • Developmental robotics
  • object exploration
  • grounding
Uneingeschränkter Zugang

Towards Autonomous Robotic Valve Turning

Online veröffentlicht: 22 Mar 2013
Seitenbereich: 17 - 26

Zusammenfassung

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.

Schlüsselwörter

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

Learning to Generalize from Demonstrations

Online veröffentlicht: 22 Mar 2013
Seitenbereich: 27 - 38

Zusammenfassung

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.

Schlüsselwörter

  • Learning by Demonstration
  • Generalized Representation
  • Graph Task Representation
  • Behavior Graphs
  • Robotics
Uneingeschränkter Zugang

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

Online veröffentlicht: 22 Mar 2013
Seitenbereich: 39 - 52

Zusammenfassung

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.

Schlüsselwörter

  • Humanoid robot walking
  • compliance
  • reinforcement learning
Uneingeschränkter Zugang

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

Online veröffentlicht: 22 Mar 2013
Seitenbereich: 53 - 65

Zusammenfassung

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.

Schlüsselwörter

  • Reinforcement learning
  • policy search
  • derivative-free optimization
  • robotics
  • autonomous underwater vehicles
Uneingeschränkter Zugang

Learning Fast Quadruped Robot Gaits with the RL PoWER Spline Parameterization

Online veröffentlicht: 22 Mar 2013
Seitenbereich: 66 - 75

Zusammenfassung

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.

Schlüsselwörter

  • Evolvable splines
  • parameterized gaits
  • HyperNEAT
  • machine learning
  • quadruped
Uneingeschränkter Zugang

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

Online veröffentlicht: 22 Mar 2013
Seitenbereich: 76 - 85

Zusammenfassung

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.

Schlüsselwörter

  • Humanoid robot
  • Reinforcement learning
  • Dynamic walking
Uneingeschränkter Zugang

Individual Recognition from Gait Using Feature Value Method

Online veröffentlicht: 22 Mar 2013
Seitenbereich: 86 - 95

Zusammenfassung

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.

Schlüsselwörter

  • Gait
  • Recognition
  • PCA
  • Feature vector
  • Exclusion method
Uneingeschränkter Zugang

A Robotized Projective Interface for Human-Robot Learning Scenarios

Online veröffentlicht: 22 Mar 2013
Seitenbereich: 96 - 106

Zusammenfassung

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.

Schlüsselwörter

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

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