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learning nonlinear multi-variate motion dynamics for real-time position and orientation control of robotic manipulators

Learning nonlinear multi-variate motion dynamics for real-ti...

引用

IEEE-RAS International Conference on Humanoid Robots

作者： E. Gribovskaya Aude Billard Learning Algorithms and Systems Laboratory Ecole Polytechnique Fédérale de Lausanne Switzerland

ISBN: (纸本)9781424445875

We present a generic framework that allows learning non-linear dynamics of motion in manipulation tasks and generating dynamical laws for control of position and orientation. This work follows a recent trend in Programming by Demonstration in which the dynamics of an arm motion is learned: position and orientation control are learned as multi-variate dynamical systems to preserve correlation within the signals. The strength of the method is three-fold: (i) it extracts dynamical control laws from demonstrations, and subsequently provides concurrent smooth control of both position and orientation; (ii) it allows to generalize a motion to unseen context; (iii) it guarantees on-line adaptation of the motion in the face of spatial and temporal perturbations. The method is validated to control a four degree of freedom humanoid arm and an industrial six degree of freedom robotic arm.

关键词： Manipulator dynamics Position control Robot kinematics Humanoid robots Motion control Humans Control systems Encoding Dynamic programming Robot programming

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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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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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learning collaborative manipulation tasks by demonstration using a haptic interface

Learning collaborative manipulation tasks by demonstration u...

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International Conference on Advanced Robotics (ICAR)

作者： Sylvain Calinon Paul Evrard Elena Gribovskaya Aude Billard Abderrahmane Kheddar Learning Algorithms and Systems Laboratory (LASA) Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland UMI3218/CRT CNRS-AIST JRL Tsukuba Japan

This paper presents a method by which a robot can learn through observation to perform a collaborative manipulation task, namely lifting an object. The task is first demonstrated by a user controlling the robot's hand via a haptic interface. learning extracts statistical redundancies in the examples provided during training by using Gaussian Mixture Regression and Hidden Markov Model. Haptic communication reflects more than pure dynamic information on the task, and includes communication patterns, which result from the two users constantly adapting their hand motion to coordinate in time and space their respective motions. We show that the proposed statistical model can efficiently encapsulate typical communication patterns across different dyads of users, that are stereotypical of collaborative behaviours between humans and robots. The proposed learning approach is generative and can be used to drive the robot's retrieval of the task by ensuring a faithful reproduction of the overall dynamics of the task, namely by reproducing the force patterns for both lift the object and adapt to the human user's hand motion. This work shows the potential that teleoperation holds for transmitting both dynamic and communicative information on the task, which classical methods for programming by demonstration have traditionally overlooked.

关键词： Collaboration Haptic interfaces Robot kinematics Hidden Markov models Humans Orbital robotics Dynamic programming Robot control Communication system control Data mining

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Teaching physical collaborative tasks: object-lifting case study with a humanoid

Teaching physical collaborative tasks: object-lifting case s...

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IEEE-RAS International Conference on Humanoid Robots

作者： Paul Evrard Elena Gribovskaya Sylvain Calinon Aude Billard Abderrahmane Kheddar CNRS-UM2 LIRMM Centre National de la Recherche Scientifique (CNRS) Montpellier France UMI3218/CRT CNRS-AIST JRL Tsukuba Japan Learning Algorithms and Systems Laboratory Switzerland Italian Institute of Technology Italy

ISBN: (纸本)9781424445875

This paper presents the application of a statistical framework that allows to endow a humanoid robot with the ability to perform a collaborative manipulation task with a human operator. We investigate to what extent the dynamics of the motion and the haptic communication process that takes place during physical collaborative tasks can be encapsulated by the probabilistic model. This framework encodes the dataset in a Gaussian Mixture Model, which components represent the local correlations across the variables that characterize the task. A set of demonstrations is performed using a bilateral coupling teleoperation setup; then the statistical model is trained in a pure follower/leader role distribution mode between the human and robot alternatively. The task is reproduced using Gaussian Mixture Regression. We present the probabilistic model and the experimental results obtained on the humanoid platform HRP-2; preliminary results assess our theory on switching behavior modes in dyad collaborative tasks: when reproduced with users which were not instructed to behave in either a follower or a leader mode, the robot switched automatically between the learned leader and follower behaviors.

关键词： Education Collaboration

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Roombots-mechanical design of self-reconfiguring modular robots for adaptive furniture

Roombots-mechanical design of self-reconfiguring modular rob...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Alexander Sproewitz Aude Billard Pierre Dillenbourg Auke Jan Ijspeert Bio-Inspired Robotics Group (BIRG) École Polytechnique Fédérale de Lausanne Lausanne Switzerland Learning Algorithms and Systems Laboratory (LASA) École Polytechnique Fédérale de Lausanne Lausanne Switzerland Centre de Recherche et d'Appui pour la Formation (CRAFT) École Polytechnique Fédérale de Lausanne Lausanne Switzerland

We aim at merging technologies from information technology, roomware, and robotics in order to design adaptive and intelligent furniture. This paper presents design principles for our modular robots, called Roombots, as future building blocks for furniture that moves and self-reconfigures. The reconfiguration is done using dynamic connection and disconnection of modules and rotations of the degrees of freedom. We are furthermore interested in applying Roombots towards adaptive behaviour, such as online learning of locomotion patterns. To create coordinated and efficient gait patterns, we use a Central Pattern Generator (CPG) approach, which can easily be optimized by any gradient-free optimization algorithm. To provide a hardware framework we present the mechanical design of the Roombots modules and an active connection mechanism based on physical latches. Further we discuss the application of our Roombots modules as pieces of a homogenic or heterogenic mix of building blocks for static structures.

关键词： Robot kinematics Intelligent robots Robotics and automation Hardware Pervasive computing Robotic assembly Merging Information technology Ubiquitous computing Augmented reality

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Evaluating the ICRA 2008 HRI challenge

Evaluating the ICRA 2008 HRI challenge

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4th ACM/IEEE International Conference on Human-Robot Interaction, HRI'09

作者： Weiss, Astrid Scherndl, Thomas Tscheligi, Manfred Billard, Aude ICTandS Center University of Salzburg Sigmund-Haffner-Gasse 18 5020 Salzburg Austria Learning Algorithms and Systems Laboratory - LASA School of Engineering - EPFL Station 9 - 1015 Lausanne Switzerland

ISBN: (纸本)9781605584041

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 dierential). The data was analyzed with respect tosix 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： Human robot interaction

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A Probabilistic Programming by Demonstration Framework Handling Constraints in Joint Space and Task Space

A Probabilistic Programming by Demonstration Framework Handl...

引用

2008 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS 2008), vol.1

作者： Sylvain Calinon Aude Billard Learning Algorithms and Systems Laboratory (LASA) Ecole Polytechnique Federale de Lausanne Lausanne Switzerland

We present a probabilistic architecture for solving generically the problem of extracting the task constraints through a Programming by Demonstration (PbD) framework and for generalizing the acquired knowledge to various situations. In previous work, we proposed an approach based on Gaussian Mixture Regression (GMR) to find a controller for the robot reproducing the essential characteristics of a skill in joint space and in task space through Lagrange optimization. In this paper, we extend this approach to a more generic procedure handling simultaneously constraints in joint space and in task space by combining directly the probabilistic representation of the task constraints with a simple Jacobian-based inverse kinematics solution. Experiments with two 5-DOFs Katana robots are presented with manipulation tasks that consist of handling and displacing a set of objects.

关键词： Joints Aerospace electronics Robots Trajectory Glass Kinematics Jacobian matrices

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A neural model of demyelination of the mouse spinal cord

A neural model of demyelination of the mouse spinal cord

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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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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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