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International Joint Conference on Neural Networks (IJCNN)

作者： Biljana Petreska Yossi Yovel Learning Algorithms and Systems Laboratory Ecole Polytechnique Federale de Lausanne Switzerland Department of Animal Physiology Tübingen University Germany

This paper presents a neural network model of demyelination of the mouse motor pathways, coupled to a central pattern generation (CPG) model for quadruped walking. Demyelination is the degradation of the myelin layer covering the axons which can be caused by several neurodegenerative autoimmune diseases such as multiple sclerosis. We use this model - to our knowledge first of its kind - to investigate the locomotion deficits that appear following demyelination of axons in the spinal cord. Our model meets several physiological and behavioral results and predicts that whereas locomotion can still occur at high percentages of demyelination damage, the distribution and location of the lesion are the most critical factors for the locomotor performance.

关键词： Nerve fibers Neurons Lesions Crosstalk Biological system modeling Oscillators Brain modeling

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learning a real time grasping strategy

Learning a real time grasping strategy

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Bidan Huang Sahar El-Khoury Miao Li Joanna J. Bryson Aude Billard Learning Algorithms and Systems Laboratory (LASA) Swiss Federal Institute of Technology Lausanne (EPFL) Switzerland Computer Science Department University of Bath UK

Real time planning strategy is crucial for robots working in dynamic environments. In particular, robot grasping tasks require quick reactions in many applications such as human-robot interaction. In this paper, we propose an approach for grasp learning that enables robots to plan new grasps rapidly according to the object's position and orientation. This is achieved by taking a three-step approach. In the first step, we compute a variety of stable grasps for a given object. In the second step, we propose a strategy that learns a probability distribution of grasps based on the computed grasps. In the third step, we use the model to quickly generate grasps. We have tested the statistical method on the 9 degrees of freedom hand of the iCub humanoid robot and the 4 degrees of freedom Barrett hand. The average computation time for generating one grasp is less than 10 milliseconds. The experiments were run in Matlab on a machine with 2.8GHz processor.

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learning of Grasp Adaptation through Experience and Tactile Sensing

Learning of Grasp Adaptation through Experience and Tactile ...

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Miao Li Yasemin Bekiroglu Danica Kragic Aude Billard Learning Algorithms and Systems Laboratory (LASA) at Ecole Polytechnique Federale de Lausanne (EPFL) Switzeland KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781479969357

To perform robust grasping, a multi-fingered robotic hand should be able to adapt its grasping configuration, i.e., how the object is grasped, to maintain the stability of the grasp. Such a change of grasp configuration is called grasp adaptation and it depends on the controller, the employed sensory feedback and the type of uncertainties inherit to the problem. This paper proposes a grasp adaptation strategy to deal with uncertainties about physical properties of objects, such as the object weight and the friction at the contact points. Based on an object-level impedance controller, a grasp stability estimator is first learned in the object frame. Once a grasp is predicted to be unstable by the stability estimator, a grasp adaptation strategy is triggered according to the similarity between the new grasp and the training examples. Experimental results demonstrate that our method improves the grasping performance on novel objects with different physical properties from those used for training.

关键词： Uncertainty Training Stability analysis Impedance Grasping Estimation Support vector machines

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On the representation of anthropomorphic robot hands: Shape versus function

On the representation of anthropomorphic robot hands: Shape ...

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IEEE-RAS International Conference on Humanoid Robots

作者： Ravin de Souza Alexandre Bernardino José Santos-Victor Aude Billard Institute for Systems and Robotics Instituto Superior Técnico Lisboa Portugal Learning Algorithms and Systems Laboratory École Polytechnique Fédéral de Lausanne Lausanne Switzerland

We address the problem of representations for anthropomorphic robot hands and their suitability for use in methods for learning or control. We approach hand configuration from the perspective of ultimate hand function and propose 2 parameterizations based on the ability of the hand to engage oppositional forces. These parameters can be extracted from grasp examples making them suitable for use in practical learning-from-demonstration frameworks. We propose a qualitative method to span hand functional space in a principled manner. This is used to construct a grasp set for evaluation and a qualitative baseline metric derived from human experience. Our results from human grasp data show that hand representations based on shape are not able to disambiguate hand-function. However, those based on hand-opposition primitives result in the widest separations among grasps that have radically different functions and can even clearly separate grasps whose functions overlap a great degree. We trust that these “functional parameterizations” can bridge the contrasting goals of task-oriented robotic grasping, that of controlling a dexterous robot hand to manifest hand-shape but with the ability to exercise specific hand-function.

关键词： Shape Grasping Thumb Vectors Robots Measurement

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On Force Synergies in Human Grasping Behavior

On Force Synergies in Human Grasping Behavior

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IEEE-RAS International Conference on Humanoid Robots

作者： Julia Starke Konstantinos Chatzilygeroudis Aude Billard Tamim Asfour Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany Learning Algorithms and Systems Laboratory Ecole Polytechnique Federale de Lausanne (EPFL) Switzerland

ISBN: (数字)9781538676301

ISBN: (纸本)9781538676318

The human hand is a versatile and complex system with dexterous manipulation capabilities. For the transfer of human grasping capabilities to humanoid robotic and prosthetic hands, an understanding of the dynamic characteristics of grasp motions is fundamental. Although the analysis of grasp synergies, especially for kinematic hand postures, is a very active field of research, the description and transfer of grasp forces is still a challenging task. In this work, we introduce a novel representation of grasp synergies in the force space, socalled force synergies, which describe forces applied at contact locations in a low dimensional space and are inspired by the correlations between grasp forces in fingers and palm. To evaluate this novel representation, we conduct a human grasping study with eight subjects performing handover and tool use tasks on 14 objects with varying content and weight using 16 different grasp types. We capture contact forces at 18 locations within the hand together with the joint angle values of a data glove with 22 degrees of freedom. We identify correlations between contact forces and derive force synergies using dimensionality reduction techniques, which allow to represent grasp forces applied during grasping with only eight parameters.

关键词： Grasping Force Task analysis Robots Correlation Thumb Force measurement

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Predicting Probabilities of Error to Combine Quantization and Early Exiting: QuEE

arXiv

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arXiv 2024年

作者： Regol, Florence Chataoui, Joud Charpentier, Bertrand Coates, Mark Piantanida, Pablo Gunnemann, Stephan McGill University Canada International Laboratory on Learning Systems Canada MILA Quebec Institute for Learning Algorithms Canada Technical University of Munich Germany École de Technologie Supérieure Canada

Machine learning models can solve complex tasks but often require significant computational resources during inference. This has led to the development of various post-training computation reduction methods that tackle this issue in different ways, such as quantization which reduces the precision of weights and arithmetic operations, and dynamic networks which adapt computation to the sample at hand. In this work, we propose a more general dynamic network that can combine both quantization and early exit dynamic network: QuEE. Our algorithm can be seen as a form of soft early exiting or input-dependent compression. Rather than a binary decision between exiting or continuing, we introduce the possibility of continuing with reduced computation. This complicates the traditionally considered early exiting problem, which we solve through a principled formulation. The crucial factor of our approach is accurate prediction of the potential accuracy improvement achievable through further computation. We demonstrate the effectiveness of our method through empirical evaluation, as well as exploring the conditions for its success on 4 classification datasets. © 2024, CC BY.

关键词： Classification (of information)

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Handling of multiple constraints and motion alternatives in a robot programming by demonstration framework

Handling of multiple constraints and motion alternatives in ...

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IEEE-RAS International Conference on Humanoid Robots

作者： Sylvain Calinon Florent D'halluin Darwin G. Caldwell Aude G. Billard Advanced Robotics Dept Italian Institute of Technology (IIT) Genova Italy Learning Algorithms and Systems Laboratory Ecole Polytechnique Federale de Lausanne Lausanne Switzerland

ISBN: (纸本)9781424445875

We consider the problem of learning robust models of robot motion through demonstration. An approach based on Hidden Markov Model (HMM) and Gaussian Mixture Regression (GMR) is proposed to extract redundancies across multiple demonstrations, and build a time-independent model of a set of movements demonstrated by a human user. Two experiments are presented to validate the method, that consist of learning to hit a ball with a robotic arm, and of teaching a humanoid robot to manipulate a spoon to feed another humanoid. The experiments demonstrate that the proposed model can efficiently handle several aspects of learning by imitation. We first show that it can be utilized in an unsupervised learning manner, where the robot is autonomously organizing and encoding variants of motion from the multiple demonstrations. We then show that the approach allows to robustly generalize the observed skill by taking into account multiple constraints in task space during reproduction.

关键词： Robot programming Hidden Markov models Robustness Humanoid robots Robot motion Humans Educational robots Education Feeds Unsupervised learning

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Motion learning and Adaptive Impedance for Robot Control during Physical Interaction with Humans

Motion Learning and Adaptive Impedance for Robot Control dur...

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2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)

作者： Elena Gribovskaya Aude Billard Abderrahmane Kheddar Learning Algorithms and Systems Laboratory (LASA) EPFLSwitzerland CNRS-UM2 LIRMM MontpellierFranceand the CNRS-AIST JRLUMI3218/CRTJapan

This article combines programming by demon- stration and adaptive control for teaching a robot to physically interact with a human in a collaborative task requiring sharing of a load by the two partners. learning a task model allows the robot to anticipate the partner’s intentions and adapt its motion according to perceived forces. As the human represents a highly complex contact environment, direct reproduction of the learned model may lead to sub-optimal results. To compen- sate for unmodelled uncertainties, in addition to learning we propose an adaptive control algorithm that tunes the impedance parameters, so as to ensure accurate reproduction. To facilitate the illustration of the concepts introduced in this paper and provide a systematic evaluation, we present experimental results obtained with simulation of a dyad of two planar 2-DOF robots.

关键词： Impedance Force Adaptation models Robot kinematics Kinematics Humans

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On the influence of symbols and myths in the responsibility ascription problem in roboethics - A roboticist’s perspective

On the influence of symbols and myths in the responsibility ...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Pierre-Andre Mudry Sarah Degallier Aude Billard Biologically Inspired Robotics Group Ecole Polytechnique Federale de Lausanne Lausanne Switzerland Learning Algorithms and Systems Laboratory Ecole Polytechnique Federale de Lausanne Lausanne Switzerland

Because of the increasing developments of humanoid robots, humans and robots are going to interact more and more often in the near future. Thus, the need for a well-defined ethical framework in which these interactions will take place is very acute. In this article, we will show why responsibility ascription is a key concept to understand today's and tomorrow's ethical issues related to human-robot interactions. By analyzing how the myths surrounding the figure of the robot in western societies have been built through centuries, we will be able to demonstrate that the question of responsibility ascription is biased in the sense that it assigns to autonomous robots a role that should be devoted to humans.

关键词： Robots

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learning complex sequential tasks from demonstration: A pizza dough rolling case study 16

Learning complex sequential tasks from demonstration: A pizz...

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ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Nadia Figueroa Ana Lucia Pais Ureche Aude Billard Learning Algorithms and Systems Laboratory (LASA) Ecole Polytechnique Fédérale de Lausanne (EPFL) Switzerland Ecole Polytechnique Federale de Lausanne Lausanne VD CH

ISBN: (纸本)9781467383707

This paper introduces a hierarchical framework that is capable of learning complex sequential tasks from human demonstrations through kinesthetic teaching, with minimal human intervention. Via an automatic task segmentation and action primitive discovery algorithm, we are able to learn both the high-level task decomposition (into action primitives), as well as low-level motion parameterizations for each action, in a fully integrated framework. In order to reach the desired task goal, we encode a task metric based on the evolution of the manipulated object during demonstration, and use it to sequence and parametrize each action primitive. We illustrate this framework with a pizza dough rolling task and show how the learned hierarchical knowledge is directly used for autonomous robot execution.

关键词： Hidden Markov models Motion segmentation Force Measurement Shape Education

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