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31st International Conference on machine learning, ICML 2014

作者： Vien, Ngo Anh Toussaint, Marc Machine Learning and Robotics Lab University of Stuttgart 70569 Germany

ISBN: (纸本)9781634393973

We consider learning and planning in relational MDPs when object existence is uncertain and new objects may appear or disappear depending on previous actions or properties of other ob-jects. Optimal policies actively need to discover objects to achieve a goal;planning in such domains in general amounts to a POMDP problem, where the belief is about the existence and properties of potential not-yet-discovered objects. We propose a computationally efficient extension of model-based relational RL methods that approximates these beliefs using discrete uncertainty predicates. In this formulation the belief update is learned using probabilistic rules and planning in the approximated belief space can be achieved using an extension of existing planners. We prove that the learned belief update rules encode an approximation of the exact belief updates of a POMDP formulation and demonstrate experimentally that the proposed approach successfully learns a set of relational rules appropriate to solve such problems. Copyright © (2014) by the International machine learning Society (IMLS) All rights reserved.

关键词： learning systems

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Reactive phase and task space adaptation for robust motion execution

Reactive phase and task space adaptation for robust motion e...

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2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Peter Englert Marc Toussaint Machine Learning and Robotics Lab Universität Stuttgart Germany

An essential aspect for making robots succeed in real-world environments is to give them the ability to robustly perform motions in continuously changing situations. Classical motion planning methods usually create plans for static environments. The direct execution of such plans in dynamic environments often becomes problematic. We present an approach that adapts motion plans by feeding changes of the environment into a transformation of the plan in task space. Furthermore, the progress in the plan is defined with a phase variable that is updated adaptively according to the actual task progress. This phase variable releases the strict time compliance that many motion planning methods bring along. The main benefit of our approach is the ability to do this adaptation in a computational efficient manner during the execution of the motion. Thus, the gap between the motion planning and motion execution stage is bridged by continuously transforming geometric and dynamic features of a reference plan to the current situation. We evaluate the performance of our approach by comparing it to alternative methods such as dynamic motion primitives and continuous replanning on several simulated benchmark tasks. Moreover, we demonstrate the real robot applicability on a PR2 robot platform.

关键词： Aerospace electronics Planning Trajectory Robot kinematics Dynamics Equations

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Entropy-based strategies for physical exploration of the environment's degrees of freedom

Entropy-based strategies for physical exploration of the env...

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2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Stefan Otte Johannes Kulick Marc Toussaint Oliver Brock Machine Learning and Robotics Lab Universität Stuttgart Germany Robotics and Biology Laboratory Technische Universität Berlin Germany

Physical exploration refers to the challenge of autonomously discovering and learning how to manipulate the environment's degrees of freedom (DOF)-by identifying promising points of interaction and pushing or pulling object parts to reveal DOF and their properties. Recent existing work focused on sub-problems like estimating DOF parameters from given data. Here, we address the integrated problem, focusing on the higher-level strategy to iteratively decide on the next exploration point before applying motion generation methods to execute the explorative action and data analysis methods to interpret the feedback. We propose to decide on exploration points based on the expected information gain, or change in entropy in the robot's current belief (uncertain knowledge) about the DOF. To this end, we first define how we represent such a belief. This requires dealing with the fact that the robot initially does not know which random variables (which DOF, and depending on their type, which DOF properties) actually exist. We then propose methods to estimate the expected information gain for an exploratory action. We analyze these strategies in simple environments and evaluate them in combination with full motion planning and data analysis in a physical simulation environment.

关键词： Joints Entropy Robots Random variables Trajectory Uncertainty Friction

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Novel 3D game-like applications driven by body interactions for learning specific forms of intangible cultural heritage 10

Novel 3D game-like applications driven by body interactions ...

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10th International Conference on Computer Vision Theory and Applications, VISAPP 2015

作者： Yilmaz, E. Uʇurca, D. Şahin, C. Dagnino, F.M. Ott, M. Pozzi, F. Dimitropoulos, K. Tsalakanidou, F. Kitsikidis, A. Al Kork, S.K. Xu, K. Denby, B. Roussel, P. Chawah, P. Buchman, L. Adda-Decker, M. Dupont, S. Picart, B. Tilmanne, J. Alivizatou, M. Hadjileontiadis, L. Charisis, V. Glushkova, A. Volioti, C. Manitsaris, A. Hemery, E. Moutarde, F. Grammalidis, N. Argedor Information Technologies Ankara Turkey Palermo Italy Information Technologies Institute Centre for Research and Technology Hellas Thessaloniki Greece Universite Pierre et Marie Curie - Paris 6 Paris France University Paris 3 Sorbonne Nouvelle Paris France Centre National de la Recherche Scientifique Laboratoire de Phonétique et Phonologie UMR 7018 Paris France Phonetics and Phonology Laboratory LPP-CNRS Paris France Universite de Mons Mons Belgium University College London London United Kingdom Aristotle University of Thessaloniki Thessaloniki Greece MTCG Lab. Dep. of Ap. Informatics University of Macedonia Thessaloniki Greece Robotics Lab. Dep. of Mathématiques et Systèmes MINES ParisTech-ARMINES Paris France Signal Processing and Machine Learning Lab. ESPCI Paris France

ISBN: (纸本)9789897580901

The main objective of the EU FP7 ICT i-Treasures project is to build a public and expandable platform to enable learning and transmission of rare know-how of intangible cultural heritage. A core part of this platform consists of game-like applications able to support teaching and learning processes in the ICH field. We have designed and developed four game-like applications (for Human Beat Box singing, Tsamiko dancing, pottery making and contemporary music composition), each corresponding to one of the ICH use cases of i-Treasures project. A first preliminary version of these applications is currently available for further validation, evaluation and demonstration within the project. We have encountered a number of issues, most of which derive from the peculiarities of the ICH domains addressed by the project, and many have already been resolved / The evaluation results are expected to lead to further optimization of these games.

关键词： Human computer interaction

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Simulation and HRI recent perspectives with the MORSE simulator

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8810卷 13-24页

作者： Lemaignan, Séverin Hanheide, Marc Karg, Michael Khambhaita, Harmish Kunze, Lars Lier, Florian Lütkebohle, Ingo Milliez, Grégoire CHILI Lab. EPFL Lausanne Switzerland Centre for Autonomous Systems University of Lincoln United Kingdom IAS Technische Universität München Germany LAAS/CNRS Université de Toulouse France Intelligent Robotics Lab. University of Birmingham United Kingdom CITEC Bielefeld University Germany Machine Learning and Robotics Lab. Universität Stuttgart Germany

Simulation in robotics is often a love-hate relationship: while simulators do save us a lot of time and effort compared to regular deployment of complex software architectures on complex hardware, simulators are also known to evade many of the real issues that robots need to manage when they enter the real world. Because humans are the paragon of dynamic, unpredictable, complex, real world entities, simulation of human-robot interactions may look condemn to fail, or, in the best case, to be mostly useless. This collective article reports on five independent applications of the MORSE simulator in the field of human-robot interaction: It appears that simulation is already useful, if not essential, to successfully carry out research in the field of HRI, and sometimes in scenarios we do not anticipate. © 2014 Springer International Publishing Switzerland.

关键词： Simulators

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Optimizing motion primitives to make symbolic models more predictive

Optimizing motion primitives to make symbolic models more pr...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Andreas Orthey Marc Toussaint Nikolay Jetchev LAAS Université de Toulouse UPS INSA INP ISAE Centre National de la Recherche Scientifique Toulouse France Machine Learning and Robotics Laboratory University of Stuttgart Stuttgart Germany Machine Learning and Robotics Laboratory FU Berlin Berlin Germany

ISBN: (纸本)9781467356404

Solving complex robot manipulation tasks requires to combine motion generation on the geometric level with planning on a symbolic level. On both levels robotics research has developed a variety of mature methodologies, including geometric motion planning and motion primitive learning on the motor level as well as logic reasoning and relational Reinforcement learning methods on the symbolic level. However, their robust integration remains a great challenge. In this paper we approach one aspect of this integration by optimizing the motion primitives on the geometric level to be as consistent as possible with their symbolic predictions. The so optimized motion primitives increase the probability of a “successful” motion-meaning that the symbolic prediction was indeed achieved. Conversely, using these optimized motion primitives to collect new data about the effects of actions the learnt symbolic rules becomes more predictive and deterministic.

关键词： Trajectory Noise Robots Grasping Cost function Linear programming

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Terrain classification with conditional random fields on fused 3D LIDAR and camera data

Terrain classification with conditional random fields on fus...

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European Conference on Mobile Robots (ECMR)

作者： Stefan Laible Yasir Niaz Khan Andreas Zell Chair of Cognitive Systems University of Tübingen Tübingen Germany Department of Machine Learning and Robotics University of Stuttgart Germany

For a mobile robot to navigate safely and efficiently in an outdoor environment, it has to recognize its surrounding terrain. Our robot is equipped with a low-resolution 3D LIDAR and a color camera. The data from both sensors are fused to classify the terrain in front of the robot. Therefore, the ground plane is divided into a grid and each cell is classified as either asphalt, cobblestones, grass or gravel. We use height and intensity features for the LIDAR data and Local ternary patterns for the image data. By additionally taking into account the context-sensitive nature of the terrain, the results can be improved significantly. We present a method based on Conditional Random Fields and compare it with a Markov Random Field based approach. We show that the Conditional Random Field model is better suited for our task. We achieve an average true positive rate of 94.2% for classifying the grid cells into the four terrain classes.

关键词： Laser radar Cameras Three-dimensional displays Image color analysis Robot sensing systems

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Caging Complex Objects with Geodesic Balls

Caging Complex Objects with Geodesic Balls

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Dmitry Zarubin Florian T. Pokorny Marc Toussaint Danica Kragic Machine Learning and Robotics Lab Universitat Stuttgart Stuttgart Germany Centre for Autonomous Systems Computer Vision and Active Perception Lab School of Computer Science and Communication KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781467363563

This paper proposes a novel approach for the synthesis of grasps of objects whose geometry can be observed only in the presence of noise. We focus in particular on the problem of generating caging grasps with a realistic robot hand simulation and show that our method can generate such grasps even on complex objects. We introduce the idea of using geodesic balls on the object's surface in order to approximate the maximal contact surface between a robotic hand and an object. We define two types of heuristics which extract information from approximate geodesic balls in order to identify areas on an object that can likely be used to generate a caging grasp. Our heuristics are based on two scoring functions. The first uses winding angles measuring how much a geodesic ball on the surface winds around a dominant axis, while the second explores using the total discrete Gaussian curvature of a geodesic ball to rank potential caging postures. We evaluate our approach with respect to variations in hand kinematics, for a selection of complex real-world objects and with respect to its robustness to noise.

关键词： Caging Complex Objects Geodesic Balls Gaussian curvature

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An efficient minimum vocabulary construction algorithm for language modeling 12

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25th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2012

作者： Lin, Sina Qin, Zengchang Huang, Zehua Wan, Tao Intelligent Computing and Machine Learning Lab. School of ASEE Beihang University Beijing China School of Advanced Engineering Beihang University China Robotics Institute Carnegie Mellon University Pittsburgh United States School of Medicine Boston University Boston United States

ISBN: (纸本)9783642310867

In learning a new word by a dictionary, we first need to know a set of "basic words" which are frequently appeared in word definitions. It often happens that you cannot understand the word you looked up because there are still some words you do not understand in its definitions or explanations provided by the dictionary. You can keep looking up these new words recursively till they all can be well explained by some basic words you already knew. How to automatically find a minimum set of such basic words to define (or recursively define) the entire vocabulary in a given dictionary is what are going to discuss in this paper. We propose an efficient algorithm to construct the Minimum Vocabulary (MV) using the word frequency information. The minimum vocabulary can be used for language modeling and experimental results demonstrate the effectiveness of using the minimum vocabulary as features in text classification. © 2012 Springer-Verlag.

关键词： Classification (of information)

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Recurrent neural state estimation in domains with long-term dependencies 20th

Recurrent neural state estimation in domains with long-term ...

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20th European Symposium on Artificial Neural Networks, Computational Intelligence and machine learning, ESANN 2012

作者： Duell, Siegmund Weichbrodt, Lina Hans, Alexander Udluft, Steffen Siemens AG Corporate Technology Intelligent Systems and Control Otto-Hahn-Ring 6 Munich81739 Germany Berlin University of Technology Machine Learning Franklinstr. 28-29 Berlin10587 Germany Otto-von-Guericke-University Magdeburg P.O.Box 4120 Magdeburg39016 Germany Ilmenau University of Technology Neuroinformatics and Cognitive Robotics Lab P.O.Box 100565 Ilmenau98684 Germany

ISBN: (纸本)9782874190490

This paper presents a state estimation approach for reinforcement learning (RL) of a partially observable Markov decision process. It is based on a special recurrent neural network architecture, the Markov decision process extraction network with shortcuts (MPEN-S). In contrast to previous work regarding this topic, we address the problem of long-term dependencies, which cause major problems in many real-world applications. The architecture is designed to model the reward-relevant dynamics of an environment and is capable to condense large sets of continuous observables to a compact Markovian state representation. The resulting estimate can be used as input for RL methods that assume the underlying system to be a Markov decision process. Although the approach was developed with RL in mind, it is also useful for general prediction tasks. © 2012, *** publication. All rights reserved.

关键词： State estimation

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