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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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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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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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EMG-based learning approach for estimating wrist motion

EMG-based learning approach for estimating wrist motion

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： S. El-Khoury I. Batzianoulis C. W. Antuvan S. Contu L. Masia S. Micera A. Billard Learning Algorithms and Systems Laboratory (LASA)at Ecole Polytechnique Federale de Lausanne (EPFL) Switzerland Assistive Robotics and Interactive Ergonomic Systems Laboratory (ARIES) at Nanyang Technological University Singapore Translational Neural Engineering Laboratory (TNE) at Ecole Polytechnique Federale de Lausanne (EPFL) Switzerland

ISBN: (纸本)9781424492695

This paper proposes an EMG based learning approach for estimating the displacement along the 2-axes (abduction/adduction and flexion/extension) of the human wrist in real-time. The algorithm extracts features from the EMG electrodes on the upper and forearm and uses Support Vector Regression to estimate the intended displacement of the wrist. Using data recorded with the arm outstretched in various locations in space, we train the algorithm so as to allow robust prediction even when the subject moves his/her arm across several positions in space. The proposed approach was tested on five healthy subjects and showed that a R index of 63.6% is obtained for generalization across different arm positions and wrist joint angles.

关键词： EMG EMG-based learning approach estimating wrist motion

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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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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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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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