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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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Linearization and identification of multiple-attractor dynamical systems through Laplacian eigenmaps

The Journal of Machine Learning Research

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The Journal of Machine learning Research 2022年第1期23卷 13290-13324页

作者： Bernardo Fichera Aude Billard Learning Algorithms and Systems Laboratory (LASA) École polytechnique fédérale de Lausanne Lausanne Switzerland

Dynamical systems (DS) are fundamental to the modeling and understanding time evolving phenomena, and have application in physics, biology and control. As determining an analytical description of the dynamics is often difficult, data-driven approaches are preferred for identifying and controlling nonlinear DS with multiple equilibrium points. Identification of such DS has been treated largely as a supervised learning problem. Instead, we focus on an unsupervised learning scenario where we know neither the number nor the type of dynamics. We propose a Graph-based spectral clustering method that takes advantage of a velocity-augmented kernel to connect data points belonging to the same dynamics, while preserving the natural temporal evolution. We study the eigenvectors and eigenvalues of the Graph Laplacian and show that they form a set of orthogonal embedding spaces, one for each sub-dynamics. We prove that there always exist a set of 2-dimensional embedding spaces in which the sub-dynamics are linear and n-dimensional embedding spaces where they are quasi-linear. We compare the clustering performance of our algorithm to Kernel K-Means, Spectral Clustering and Gaussian Mixtures and show that, even when these algorithms are provided with the correct number of sub-dynamics, they fail to cluster them correctly. We learn a diffeomorphism from the Laplacian embedding space to the original space and show that the Laplacian embedding leads to good reconstruction accuracy and a faster training time through an exponential decaying loss compared to the state-of-the-art diffeomorphism-based approaches.

关键词： dynamical system graph Laplacian clustering learning

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learning to control planar hitting motions in a minigolf-like task

Learning to control planar hitting motions in a minigolf-lik...

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2011 IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Klas Kronander Mohammad S.M. Khansari-Zadeh Aude Billard Learning Algorithms and Systems Laboratory (LASA) Ecole Polytechnique Federale de Lausanne Switzerland

A current trend in robotics is to define robot tasks using a combination of superimposed motion patterns. For maximum versatility of such motion patterns, they should be easily and efficiently adaptable for situations beyond those for which the motion was originally designed. In this work, we show how a challenging minigolf-like task can be efficiently learned by the robot using a basic hitting motion model and a task-specific adaptation of the hitting parameters: hitting speed and hitting angle. We propose an approach to learn the hitting parameters for a minigolf field using a set of provided examples. This is a non-trivial problem since the successful choice of hitting parameters generally represent a highly non-linear, multi-valued map from the situation-representation to the hitting parameters. We show that by limiting the problem to learning one combination of hitting parameters for each input, a high-performance model of the hitting parameters can be learned using only a small set of training data. We compare two statistical methods, Gaussian Process Regression (GPR) and Gaussian Mixture Regression (GMR) in the context of inferring hitting parameters for the minigolf task. We validate our approach on the 7 degrees of freedom Barrett WAM robotic arm in both a simulated and real environment.

关键词： Robots Training Vectors Ground penetrating radar Training data Trajectory Sports equipment

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Transfer in Inverse Reinforcement learning for Multiple Strategies

Transfer in Inverse Reinforcement Learning for Multiple Stra...

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Ajay Kumar Tanwani Aude Billard Learning Algorithms and Systems Laboratory (LASA) Ecole Polytechnique Federale de Lausanne Switzerland

ISBN: (纸本)9781467363563

We consider the problem of incrementally learning different strategies of performing a complex sequential task from multiple demonstrations of an expert or a set of experts. While the task is the same, each expert differs in his/her way of performing it. We assume that this variety across experts' demonstration is due to the fact that each expert/strategy is driven by a different reward function, where reward function is expressed as a linear combination of a set of known features. Consequently, we can learn all the expert strategies by forming a convex set of optimal deterministic policies, from which one can match any unseen expert strategy drawn from this set. Instead of learning from scratch every optimal policy in this set, the learner transfers knowledge from the set of learned policies to bootstrap its search for new optimal policy. We demonstrate our approach on a simulated mini-golf task where the 7 degrees of freedom Barrett WAM robot arm learns to sequentially putt on different holes in accordance with the playing strategies of the expert.

关键词： Multiple Strategies WAM playing strategies

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learning Coupled Dynamical systems from human demonstration for robotic eye-arm-hand coordination

Learning Coupled Dynamical Systems from human demonstration ...

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IEEE-RAS International Conference on Humanoid Robots

作者： Luka Lukic José Santos-Victor Aude Billard Learning Algorithms and Systems Laboratory (LASA) EPFL Switzerland Computer Vision Laboratory (VISLAB) IST Portugal

Efficient, adaptive and reliable visuomotor control system is crucial to enable robots to display flexibility in the face of changes in the environment. This paper takes inspiration in human eye-arm-hand coordination pattern to develop an equivalently robust robot controller. We recorded gaze, arm, hand, and trunk data from human subjects in reaching and grasping scenarios with/without obstacle in the workspace. An eye-arm-hand controller is developed, based on our extension of Coupled Dynamical systems (CDS). We exploit the time-invariant properties of the CDS to allow fast adaptation to spatial and temporal perturbations during task completion. CDS global stability guarantees that the eye, the arm and the hand will reach the target in retinal, operational and grasp space respectively. When facing perturbations, the system can re-plan its actions almost instantly, without the need for an additional planning module. Coupling profiles for eye-arm and arm-hand systems can be modulated allowing to adjust the behavior of each slave system with respect to control signals flowing from the corresponding master system. We show how the CDS eye-arm-hand control framework can be used to handle the presence of obstacles in the workspace. The eye-arm-hand controller is validated in a series of experiments conducted with the iCub robot.

关键词： Robot kinematics Couplings Heuristic algorithms

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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...

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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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Dynamical System Approach to Navigation Around Obstacles

Dynamical System Approach to Navigation Around Obstacles

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European Control Conference (ECC)

作者： Aradhana Nayak Aude Billard Learning algorithms and systems laboratory (LASA) Ecole Polytechnique Federale de Lausanne (EPFL) Switzerland

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

In this article, we propose a dynamical system to avoid obstacles which are star shaped and simultaneously converge to a goal. The convergence is almost-global in a domain and the stationary points are identified explicitly. Our approach is based on the idea that an ideal vector field which avoids the obstacle traverses its boundary up to when a clear path to the goal is available. We show the existence of this clear path through a set connecting the boundary of the obstacle and the goal. The traversing vector field is determined for an arbitrary obstacle (de-scribed by a set of points) by separating it into cluster of stars. We propose an algorithm which is linear in number of points inside the obstacle. We verify the theoretical results presented with various hand drawn obstacle sets. Our methodology is also extended to obstacles which are not star-shaped, and, those which exist in high dimensions.

关键词： Support vector machines Navigation Shape Level set Heuristic algorithms Stars Europe

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Online learning of varying stiffness through physical human-robot interaction

Online learning of varying stiffness through physical human-...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Klas Kronander Aude Billard Learning Algorithms and Systems Laboratory (LASA) School of Engineering Ecole Polytechnique Federale de Lausanne Switzerland

Programming by Demonstration offers an intuitive framework for teaching robots how to perform various tasks without having to preprogram them. It also offers an intuitive way to provide corrections and refine teaching during task execution. Previously, mostly position constraints have been taken into account when teaching tasks from demonstrations. In this work, we tackle the problem of teaching tasks that require or can benefit from varying stiffness. This extension is not trivial, as the teacher needs to have a way of communicating to the robot what stiffness it should use. We propose a method by which the teacher can modulate the stiffness of the robot in any direction through physical interaction. The system is incremental and works online, so that the teacher can instantly feel how the robot learns from the interaction. We validate the proposed approach on two experiments on a 7-Dof Barrett WAM arm.

关键词： Robots Education Impedance Humans Trajectory Covariance matrix Kinematics

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Active Teaching in Robot Programming by Demonstration

Active Teaching in Robot Programming by Demonstration

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IEEE International Workshop on Robot and Human Communication (ROMAN)

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

Robot programming by demonstration (RbD) covers methods by which a robot learns new skills through human guidance. In this work, we take the perspective that the role of the teacher is more important than just being a model of successful behaviour, and present a probabilistic framework for RbD which allows to extract incrementally the essential characteristics of a task described at a trajectory level. To demonstrate the feasibility of our approach, we present two experiments where manipulation skills are transferred to a humanoid robot by means of active teaching methods that put the human teacher in the loop of the robot's learning. The robot first observes the task performed by the user (through motion sensors) and the robot's skill is then refined progressively by embodying the robot and putting it through the motion (kinesthetic teaching).

关键词： Education Robot programming Educational robots Robot sensing systems Human robot interaction Humanoid robots Biological system modeling Laboratories Joints Problem-solving

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Augmented-SVM: Automatic space partitioning for combining multiple non-linear dynamics 12

Augmented-SVM: Automatic space partitioning for combining mu...

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Annual Conference on Neural Information Processing systems

作者： Ashwini Shukla Aude Billard Learning Algorithms and Systems Laboratory (LASA) Ecole Polytechnique Federate de Lausanne (EPFL) Lausanne Switzerland-1015

ISBN: (纸本)9781627480031

Non-linear dynamical systems (DS) have been used extensively for building generative models of human behavior. Their applications range from modeling brain dynamics to encoding motor commands. Many schemes have been proposed for encoding robot motions using dynamical systems with a single attractor placed at a predefined target in state space. Although these enable the robots to react against sudden perturbations without any re-planning, the motions are always directed towards a single target. In this work, we focus on combining several such DS with distinct attractors, resulting in a multi-stable DS. We show its applicability in reach-to-grasp tasks where the attractors represent several grasping points on the target object. While exploiting multiple attractors provides more flexibility in recovering from unseen perturbations, it also increases the complexity of the underlying learning problem. Here we present the Augmented-SVM (A-SVM) model which inherits region partitioning ability of the well known SVM classifier and is augmented with novel constraints derived from the individual DS. The new constraints modify the original SVM dual whose optimal solution then results in a new class of support vectors (SV). These new SV ensure that the resulting multi-stable DS incurs minimum deviation from the original dynamics and is stable at each of the attractors within a finite region of attraction. We show, via implementations on a simulated 10 degrees of freedom mobile robotic platform, that the model is capable of real-time motion generation and is able to adapt on-the-fly to perturbations.

关键词： Human behavior perturbation Robot kinematics Attractor region of attraction DYNAMICAL systems minimum deviation Nonlinear Dynamics state space Support Vector Network optimal solution Robot Degree of freedom

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