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共 35 条记录，以下是11-20 订阅

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Tactile guidance for policy refinement and reuse

Tactile guidance for policy refinement and reuse

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IEEE International Conference on Development and learning, ICDL

作者： Brenna D. Argall Eric L. Sauser Aude G. Billard Learning Algorithms and Systems Laboratory (LASA) École Polytechnique Fédérale de Lausanne Lausanne Switzerland

Demonstration learning is a powerful and practical technique to develop robot behaviors. Even so, development remains a challenge and possible demonstration limitations can degrade policy performance. This work presents an approach for policy improvement and adaptation through a tactile interface located on the body of a robot. We introduce the Tactile Policy Correction (TPC) algorithm, that employs tactile feedback for the refinement of a demonstrated policy, as well as its reuse for the development of other policies. We validate TPC on a humanoid robot performing grasp-positioning tasks. The performance of the demonstrated policy is found to improve with tactile corrections. Tactile guidance also is shown to enable the development of policies able to successfully execute novel, undemonstrated, tasks.

关键词： Humans Robot kinematics Manipulators Tactile sensors Joints

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Efficient Configuration Exploration in Inverse Dynamics Acquisition of Robotic Manipulators

Efficient Configuration Exploration in Inverse Dynamics Acqu...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Farshad Khadivar Sthithparagya Gupta Walid Amanhoud Aude Billard Learning Algorithms and Systems Laboratory (LASA) Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland

The inverse dynamics of a robotic manipulator is instrumental in precise robot control and manipulation. However, acquiring such a model is challenging, not only due to unmodelled non-linearities such as joint friction, but also from a machine learning perspective (e.g., input space dimension, amount of data needed). The accuracy of such models, regardless of the learning techniques, relies on proper excitation and exploration of the robot’s configuration space, in order to collect a rich dataset. This study aims to provide rich data in learning the inverse dynamics of a serial robotic manipulator using supervised machine learning techniques. We propose a method, called Max-Information Configuration Exploration (MICE), to incrementally explore and generate information-rich data via computing parameters of a trajectory set. We also introduce a new set of excitation trajectories that explores robot’s configuration through imposed stable limit cycles in robot joints’ phase space while satisfying feasibility constraints and physical bounds. We benchmark MICE against state-of-the-art in terms of data quality and learning accuracy. The proposed methodology for data collection, model learning, and evaluation, is validated with a KUKA IIWA14 robotic arm where the results prove significant improvement over traditional approaches.

关键词： Trajectory tracking Robot control Limit-cycles Machine learning Mice Data models Robustness

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Policy adaptation with tactile feedback

Policy adaptation with tactile feedback

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

作者： Brenna D. Argall Eric L. Sauser Aude G. Billard Learning Algorithms and Systems Laboratory (LASA) Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland

Behavior adaptation with execution experience is a practical feature for any policy learning system. Our work provides performance feedback to a robot learner in the form of tactile corrections from a human teacher, for the purpose of policy refinement as well as policy reuse. Multiple variants of our general approach have been validated on the iCub robot, as building blocks towards a high-DoF humanoid system that integrates tactile sensing on the hands and arms into complex behaviors and sophisticated learning routines.

关键词： Humans Adaptation models Tactile sensors Laboratories

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learning Dynamical System Modulation for Constrained Reaching Tasks

Learning Dynamical System Modulation for Constrained Reachin...

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IEEE-RAS International Conference on Humanoid Robots

作者： Micha Hersch Florent Guenter Sylvain Calinon Aude G. Billard Learning Algorithms and Systems Laboratory-LASA School of Engineering École Polytechnique Fédérale de Lausanne Lausanne Switzerland

ISBN: (纸本)1424401992

In this paper we combine kinesthetic demonstrations and dynamical systems to enable a humanoid robot to imitate constrained reaching gestures directed toward a target. Using a learning algorithm based on Gaussian mixture regression, the task constraints are extracted from several demonstrations. Those constraints take the form of desired velocity profiles for the end-effector and joint angle variables, with associated covariance matrices describing the variations allowed around the desired profiles. Those constraints are then used to modulate a dynamical system which has the reaching target as attractor. This way, the reaching trajectory can be reshaped in order to satisfy the constraints of the task, while preserving the adaptability and robustness provided by the dynamical system. In particular, the system can adapt to changes in the initial conditions and to target displacements occurring during the movement execution. We first evaluate the potential of this method on experiments involving the Hoap3 humanoid robot putting an object into a box. We then show how a manipulation tasks can be executed as sequences of such constrained reaching movement. This is illustrated on a packaging task performed by the robot

关键词： Humanoid robots Service robots Humans Robot control Laboratories Covariance matrix Robustness Packaging Electrical equipment industry Robot programming

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Design of a Biomimetic Spine for the Humanoid Robot Robota

Design of a Biomimetic Spine for the Humanoid Robot Robota

引用

IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： L. Roos F. Guenter A. Guignard A.G. Billard Learning Algorithms and Systems Laboratory-LASA School of Engineering Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland

This paper presents a prototype of 3 degrees-of-freedom articulated spine for the doll-shaped humanoid robot Robota. This work follows an approach that emphasizes the need for a high human-likeliness in both the external features of the robot and in the kinematics of its motions to enhance human-robot interactions. The design of a spinal cord for our humanoid robot satisfies both criteria in providing offering a smooth human-like parallel means of bending forward and sideways

关键词： Humanoid robots Biomimetics Prototypes Educational robots Torso Education Laboratories Human robot interaction Autism Service robots

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Probabilistic depth image registration incorporating nonvisual information

Probabilistic depth image registration incorporating nonvisu...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Manuel Wüthrich Peter Pastor Ludovic Righetti Aude Billard Stefan Schaal Faculty of Micro Engineering EPF Lausanne Switzerland Computational Learning and Motor Control Laboratory (CLMC) USC USA Learning Algorithms and Systems Laboratory (LASA) EPF Lausanne Switzerland

In this paper, we derive a probabilistic registration algorithm for object modeling and tracking. In many robotics applications, such as manipulation tasks, nonvisual information about the movement of the object is available, which we will combine with the visual information. Furthermore we do not only consider observations of the object, but we also take space into account which has been observed to not be part of the object. Furthermore we are computing a posterior distribution over the relative alignment and not a point estimate as typically done in for example Iterative Closest Point (ICP). To our knowledge no existing algorithm meets these three conditions and we thus derive a novel registration algorithm in a Bayesian framework. Experimental results suggest that the proposed methods perform favorably in comparison to PCL [1] implementations of feature mapping and ICP, especially if nonvisual information is available.

关键词： Iterative closest point algorithm Cameras Robots Shape Covariance matrix Visualization Approximation algorithms

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WearCam: A head mounted wireless camera for monitoring gaze attention and for the diagnosis of developmental disorders in young children

WearCam: A head mounted wireless camera for monitoring gaze ...

引用

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Lorenzo Piccardi Basilio Noris Olivier Barbey Aude Billard Giuseppina Schiavone Flavio Keller Claes von Hofsten Learning Algorithms & Systems Laboratory LASA EPF Lausanne Lausanne Switzerland DNNP Laboratory Roma Italy Department of Psychology University of Uppsala Uppsala Sweden

Autism covers a large spectrum of disorders that affect the individual's way of interacting socially and is often revealed by the individual's lack of interest in gazing at human faces. Currently Autism is diagnosed in children no younger than 2 years old. This paper presents a new monitoring device, the WearCam, to help forming a diagnosis of this neurodevelopmental disorder at an earlier age than currently possible. The WearCam consists of a wireless camera located on the forefront of the child. The WearCam collects videos from the viewpoint of the child's head. Color detection, face detection and gaze detection are run on the data in order to locate the approximate gaze direction of the child and determine where her attention is drawn to (persons, objects, etc.). We report on early tests of the camera within normally developing children. Firstly the technical characteristics of the current prototype of the WearCam will be described. Afterwards the type of data collected with this device with young children will be shown.

关键词： Cameras Autism Face detection Prototypes Biomedical monitoring Batteries Humans Videos Object detection Charge-coupled image sensors

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Evaluating the ICRA 2008 HRI challenge

Evaluating the ICRA 2008 HRI challenge

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

作者： Astrid Weiss Thomas Scherndl Manfred Tscheligi Aude Billard ICT&S Center University of Salzburg Salzburg Austria Learning Algorithms and Systems Laboratory LASA School of Engineering Lausanne Switzerland

This paper reports on the evaluation of the ICRA 2008 Human-Robot Interaction (HRI) Challenge. Five research groups demonstrated state-of-the-art work on HRI with a special focus on social and learning abilities. The demonstrations were rated by expert evaluators, in charge of awarding the prize, and 269 participants, i.e. 20 percent of the conference attendees through a standardized questionnaire (semantic differential). The data was analyzed with respect to six independent variables: expert evaluators vs. attendees, nationality of participants, origin region of the demo, age, gender and knowledge level of the attendees. Conference attendees tended to give higher scores for Social Skills, General Impression, and Overall Score than the expert evaluators. Irrespectively of the level of knowledge, age, and gender, conference attendees rated all demos relatively homogeneously. However, a comparative analysis of the conference attendees's ratings nationality-wise showed that demonstrations were rated differently depending on the region of origin. Conference attendees for the USA and Asian countries tended to rate demos from the same country of origin more frequently and more positively.

关键词： Robots Europe Educational institutions Conferences Automation Humans Asia

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learning a real time grasping strategy

Learning a real time grasping strategy

引用

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 ...

引用

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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