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Robotics & Automation Magazine, IEEE

Issue 2 • Date June 2010

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Displaying Results 1 - 25 of 28
  • [Front cover]

    Publication Year: 2010 , Page(s): C1
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  • Table of contents

    Publication Year: 2010 , Page(s): 1
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  • What Makes Our Magazine Different? [From the Editor's Desk]

    Publication Year: 2010 , Page(s): 2 - 18
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  • The TA Strategic Plan [President' Message]

    Publication Year: 2010 , Page(s): 4 - 18
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  • Robots in Military and Aerospace Technologies [News and Views]

    Publication Year: 2010 , Page(s): 6
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  • The ICRA Robot Challenge [Competitions]

    Publication Year: 2010 , Page(s): 8 - 10
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  • The Premier Meeting of IFRR [IFRR Update]

    Publication Year: 2010 , Page(s): 11
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  • Sharing Software with ROS [ROS Topics]

    Publication Year: 2010 , Page(s): 12 - 14
    Cited by:  Papers (5)  |  Patents (1)
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  • People Meeting Robots in the Workplace [Industrial Activities]

    Publication Year: 2010 , Page(s): 15 - 16
    Cited by:  Papers (2)
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  • Robot Learning in Practice [From the Guest Editors]

    Publication Year: 2010 , Page(s): 17 - 18
    Cited by:  Papers (2)
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  • 6th IEEE International Conference on Automation Science and Engineering

    Publication Year: 2010 , Page(s): 19
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  • Learning Control in Robotics

    Publication Year: 2010 , Page(s): 20 - 29
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2757 KB) |  | HTML iconHTML  

    Recent trends in robot learning are to use trajectory-based optimal control techniques and reinforcement learning to scale complex robotic systems. On the one hand, increased computational power and multiprocessing, and on the other hand, probabilistic reinforcement learning methods and function approximation, have contributed to a steadily increasing interest in robot learning. Imitation learning has helped significantly to start learning with reasonable initial behavior. However, many applications are still restricted to rather lowdimensional domains and toy applications. Future work will have to demonstrate the continual and autonomous learning abilities, which were alluded to in the introduction. View full abstract»

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  • Learning Actions from Observations

    Publication Year: 2010 , Page(s): 30 - 43
    Cited by:  Papers (13)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4866 KB) |  | HTML iconHTML  

    In the area of imitation learning, one of the important research problems is action representation. There has been a growing interest in expressing actions as a combination of meaningful subparts called action primitives. Action primitives could be thought of as elementary building blocks for action representation. In this article, we present a complete concept of learning action primitives to recognize and synthesize actions. One of the main novelties in this work is the detection of primitives in a unified framework, which takes into account objects and actions being applied to them. As the first major contribution, we propose an unsupervised learning approach for action primitives that make use of the human movements as well as object state changes. As the second major contribution, we propose using parametric hidden Markov models (PHMMs) for representing the discovered action primitives. PHMMs represent movement trajectories as a function of their desired effect on the object, and we will discuss 1) how these PHMMs can be trained in an unsupervised manner, 2) how they can be used for synthesizing movements to achieve a desired effect, and 3) how they can be used to recognize an action primitive and the effect from an observed acting human. View full abstract»

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  • Learning and Reproduction of Gestures by Imitation

    Publication Year: 2010 , Page(s): 44 - 54
    Cited by:  Papers (34)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2973 KB) |  | HTML iconHTML  

    We presented and evaluated an approach based on HMM, GMR, and dynamical systems to allow robots to acquire new skills by imitation. Using HMM allowed us to get rid of the explicit time dependency that was considered in our previous work [12], by encapsulating precedence information within the statistical representation. In the context of separated learning and reproduction processes, this novel formulation was systematically evaluated with respect to our previous approach, LWR [20], LWPR [21], and DMPs [13]. We finally presented applications on different kinds of robots to highlight the flexibility of the proposed approach in three different learning by imitation scenarios. View full abstract»

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  • Imitation and Reinforcement Learning

    Publication Year: 2010 , Page(s): 55 - 62
    Cited by:  Papers (15)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3159 KB) |  | HTML iconHTML  

    In this article, we present both novel learning algorithms and experiments using the dynamical system MPs. As such, we describe this MP representation in a way that it is straightforward to reproduce. We review an appropriate imitation learning method, i.e., locally weighted regression, and show how this method can be used both for initializing RL tasks as well as for modifying the start-up phase in a rhythmic task. We also show our current best-suited RL algorithm for this framework, i.e., PoWER. We present two complex motor tasks, i.e., ball-in-a-cup and ball paddling, learned on a real, physical Barrett WAM, using the methods presented in this article. Of particular interest is the ball-paddling application, as it requires a combination of both rhythmic and discrete dynamical systems MPs during the start-up phase to achieve a particular task. View full abstract»

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  • A Mine on Its Own

    Publication Year: 2010 , Page(s): 63 - 73
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    This article presents a study of Gaussian process (GP) models applied to the problems of modeling and data fusion in the context of large-scale terrain modeling. The proposed model naturally provides a multiresolution representation of space, incorporates and handles uncertainties aptly, and copes with incompleteness of sensory information. These attributes are considered essential to support most field robotics applications, including autonomous mining. GP regression techniques are applied to estimate and interpolate (to fill gaps in occluded areas) elevation information across the field. GP approximation methods are introduced to enable the application of the proposed techniques to large data sets. To obtain a comprehensive model of complex terrain, typically, multiple sensory modalities and multiple data sets are required. The GP modeling approach is consequently extended to fuse multiple, multimodal data sets to obtain a best estimate of the elevation given the individual data sets. Two different GP-based concepts are applied to perform data fusion-heteroscedastic GPs and dependent GPs (DGPs). Thus, this article presents a report on an ongoing study of the use of GPs and several GPbased concepts to the problem of large-scale terrain modeling in the context of mining automation. View full abstract»

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  • Learning for Autonomous Navigation

    Publication Year: 2010 , Page(s): 74 - 84
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (6211 KB) |  | HTML iconHTML  

    Autonomous navigation by a mobile robot through L natural, unstructured terrain is one of the premier k challenges in field robotics. Tremendous advances V in autonomous navigation have been made recently in field robotics. Machine learning has played an increasingly important role in these advances. The Defense Advanced Research Projects Agency (DARPA) UGCV-Perceptor Integration (UPI) program was conceived to take a fresh approach to all aspects of autonomous outdoor mobile robot design, from vehicle design to the design of perception and control systems with the goal of achieving a leap in performance to enable the next generation of robotic applications in commercial, industrial, and military applications. The essential problem addressed by the UPI program is to enable safe autonomous traverse of a robot from Point A to Point B in the least time possible given a series of waypoints in complex, unstructured terrain separated by 0.2-2 km. To accomplish this goal, machine learning techniques were heavily used to provide robust and adaptive performance, while simultaneously reducing the required development and deployment time. This article describes the autonomous system, Crusher, developed for the UPI program and the learning approaches that aided in its successful performance. View full abstract»

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  • Human–Robot Interaction

    Publication Year: 2010 , Page(s): 85 - 89
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2263 KB) |  | HTML iconHTML  

    The field of human-robot interaction (HRI) addresses the design, understanding, and evaluation of robotic systems, which involve humans and robots interacting through communication. As the field matures, education of students becomes increasingly important. Courses in HRI provide the canonical set of knowledge and core skills that represent the current state of the field and permit the evolution of knowledge and methods to be transferred from research to a broad set of students. In addition, coursework in HRI creates a workforce capable of transferring HRI theory to practice. However, as would be expected with an emerging field, HRI courses are largely ad hoc. This article summarizes the discussion and findings from the "Teaching Humans About Human-Robot Interaction" workshop on the development of an HRI course for computer scientists and engineers. View full abstract»

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  • Guided Chaos

    Publication Year: 2010 , Page(s): 90 - 98
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    In this article, we have presented, in what we believe to be the first of its kind, the design and simulated testing of automated path planning and control strategies for a fixed-wing UAV executing an unpowered descent for landing during an emergency. Simulated test results demonstrate the ability of the gliding aircraft to follow the prescribed path in changing winds, with average path deviation errors that are comparable to or even better than that of manned, powered aircraft. To further verify the performances of our algorithms, we are currently preparing for flight tests with a Boomerang UAV. View full abstract»

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  • If You Are Not Afraid… [Student's Corner]

    Publication Year: 2010 , Page(s): 99 - 100
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  • GigaPixels for Science [Education]

    Publication Year: 2010 , Page(s): 101 - 104
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  • The IEEE RAS Tunisia Chapter: A Rising and Promising Team [Regional]

    Publication Year: 2010 , Page(s): 103 - 104
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  • Roboethics [TC Spotlight]

    Publication Year: 2010 , Page(s): 105 - 109
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  • New Chapters of RAS [Society News]

    Publication Year: 2010 , Page(s): 106
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  • 2011 IEEE International Conference on Robotics and Automation

    Publication Year: 2010 , Page(s): 30
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Aims & Scope

IEEE Robotics and Automation Magazine is a unique technology publication which is peer-reviewed, readable and substantive.  The Magazine is a forum for articles which fall between the academic and theoretical orientation of scholarly journals and vendor sponsored trade publications.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Eugenio Guglielmelli
Laboratory of Biomedical Robotics
      and Biomicrosystems
Universita' Campus Bio-Medico
      di Roma