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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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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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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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iScience 2023年第9期26卷 107524页

作者： Iwane, Fumiaki Sobolewski, Aleksander Chavarriaga, Ricardo Millán, José del R. Department of Electrical and Computer Engineering The University of Texas at Austin Austin 78712 TX United States Department of Neurology The University of Texas at Austin Austin 78712 TX United States Learning Algorithms and Systems Laboratory (LASA) École Polytechnique Fédérale de Lausanne (EPFL) Lausanne 1015 Switzerland Wyss Center for Bio and Neuroengineering Campus Biotech Genève 1202 Switzerland École Polytechnique Fédérale de Lausanne (EPFL) Campus Biotech Genève 1202 Switzerland Centre for Artificial Intelligence Zurich University of Applied Sciences (ZHAW) Winterthur 8401 Switzerland Mulva Clinic for the Neurosciences The University of Texas at Austin Austin 78712 TX United States

Error-related potentials (ErrPs) are a prominent electroencephalogram (EEG) correlate of performance monitoring, and so crucial for learning and adapting our behavior. It is poorly understood whether ErrPs encode further information beyond error awareness. We report an experiment with sixteen participants over three sessions in which occasional visual rotations of varying magnitude occurred during a cursor reaching task. We designed a brain-computer interface (BCI) to detect ErrPs that provided real-time feedback. The individual ErrP-BCI decoders exhibited good transfer across sessions and scalability over the magnitude of errors. A non-linear relationship between the ErrP-BCI output and the magnitude of errors predicts individual perceptual thresholds to detect errors. We also reveal theta-gamma oscillatory coupling that co-varied with the magnitude of the required adjustment. Our findings open new avenues to probe and extend current theories of performance monitoring by incorporating continuous human interaction tasks and analysis of the ErrP complex rather than individual peaks. © 2023 The Author(s)

关键词： Cognitive neuroscience Neuroscience

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Control Interface for Hands-free Navigation of Standing Mobility Vehicles based on Upper-Body Natural Movements

Control Interface for Hands-free Navigation of Standing Mobi...

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2020 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Yang Chen Diego Paez-Granados Hideki Kadone Kenji Suzuki School of Integrative and Global Majors (SIGMA) University of Tsukuba Japan Learning Algorithms and Systems Laboratory (LASA) Ecole Polytechnique Federale de Lausanne (EPFL) Lausanne Switzerland Center for Innovative Medicine and Engineering University of Tsukuba Hospital Japan Faculty of Engineering and Center for Cybernics Research University of Tsukuba Japan

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

In this paper, we propose and evaluate a novel human-machine interface (HMI) for controlling a standing mobility vehicle or person carrier robot, aiming for a hands-free control through upper-body natural postures derived from gaze tracking while walking. We target users with lower-body impairment with remaining upper-body motion capabilities. The developed HMI bases on a sensing array for capturing body postures; an intent recognition algorithm for continuous mapping of body motions to robot control space; and a personalizing system for multiple body sizes and shapes. We performed two user studies: first, an analysis of the required body muscles involved in navigating with the proposed control; and second, an assessment of the HMI compared with a standard joystick through quantitative and qualitative metrics in a narrow circuit task. We concluded that the main user control contribution comes from Rectus Abdominis and Erector Spinae muscle groups at different levels. Finally, the comparative study showed that a joystick still outperforms the proposed HMI in usability perceptions and controllability metrics, however, the smoothness of user control was similar in jerk and fluency. Moreover, users' perceptions showed that hands-free control made it more anthropomorphic, animated, and even safer.

关键词： Measurement Legged locomotion Navigation Muscles Robot sensing systems Usability Task analysis

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Evaluation of an Industrial Robotic Assistant in an Ecological Environment

Evaluation of an Industrial Robotic Assistant in an Ecologic...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Baptiste Busch Giuseppe Cotugno Mahdi Khoramshahi Grigorios Skaltsas Dario Turchi Leonardo Urbano Mirko Wächter You Zhou Tamim Asfour Graham Deacon Duncan Russell Aude Billard EPFL Learning Algorithms and Systems Laboratory (LASA) Lausanne Switzerland Robotics Research Group of Ocado Technology Hatfield UK University of Hertfordshire Hatfield UK KIT High Performance Humanoid Technologies Lab (H2T) Karlsruhe Germany

Social robotic assistants have been widely studied and deployed as telepresence tools or caregivers. Evaluating their design and impact on the people interacting with them is of prime importance. In this research, we evaluate the usability and impact of ARMAR-6, an industrial robotic assistant for maintenance tasks. For this evaluation, we have used a modified System Usability Scale (SUS) to assess the general usability of the robotic system and the Godspeed questionnaire series for the subjective perception of the coworker. We have also recorded the subjects' gaze fixation patterns and analyzed how they differ when working with the robot compared to a human partner.

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A roadmap for AI in robotics

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Nature Machine Intelligence 2025年 1-7页

作者： Aude Billard Alin Albu-Schaeffer Michael Beetz Wolfram Burgard Peter Corke Matei Ciocarlie Ravinder Dahiya Danica Kragic Ken Goldberg Yukie Nagai Davide Scaramuzza Learning Algorithms and Systems Laboratory (LASA) École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland Institute of Robotics and Mechatronics DLR-German Aerospace Center/Department of Informatics Technical University of Munich Munich Germany Institute for Artificial Intelligence Computer Science Department University of Bremen Bremen Germany Department of Engineering University of Technology Nuremberg Nuremberg Germany Center for Robotics Queensland University of Technology Brisbane Queensland Australia Columbia University New York City NY USA Northeastern University Boston MA USA School of Computer Science and Communication Royal Institute of Technology Stockholm Sweden University of California Berkeley Berkeley CA USA International Research Center for Neurointelligence The University of Tokyo Tokyo Japan Robotics and Perception Group University of Zurich Zurich Switzerland

There is growing excitement about the potential of leveraging artificial intelligence (AI) to tackle some of the outstanding barriers to the full deployment of robots in daily lives. However, action and sensing in the physical world pose greater and different challenges for AI than analysing data in isolation and it is important to reflect on which AI approaches are most likely to be successfully applied to robots. Questions to address, among others, are how AI models can be adapted to specific robot designs, tasks and environments. This Perspective offers an assessment of what AI has achieved for robotics since the 1990s and proposes a research roadmap with challenges and promises. These range from keeping up-to-date large datasets, representatives of a diversity of tasks that robots may have to perform, and of environments they may encounter, to designing AI algorithms tailored specifically to robotics problems but generic enough to apply to a wide range of applications and transfer easily to a variety of robotic platforms. For robots to collaborate effectively with humans, they must predict human behaviour without relying on bias-based profiling. Explainability and transparency in AI-driven robot control are essential for building trust, preventing misuse and attributing responsibility in accidents. We close with describing what are, in our view, primary long-term challenges, namely, designing robots capable of lifelong learning, and guaranteeing safe deployment and usage, as well as sustainable development.

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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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On the Evolution of Fingertip Grasping Manifolds

On the Evolution of Fingertip Grasping Manifolds

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IEEE International Conference on Robotics and Automation

作者： K. Hang J. A. Haustein M. Li A. Billard C. Smith D. Kragic the Computer Vision and Active Perception Lab CAS CSC at KTH Royal Institute of Technology Stockholm Sweden the Learning Algorithms and Systems Laboratory (LASA) at école Polytechnique Fédérale de Lausanne (EPFL) Switzerland

ISBN: (纸本)9781467380270

Efficient and accurate planning of fingertip grasps is essential for dexterous in-hand manipulation. In this work, we present a system for fingertip grasp planning that incrementally learns a heuristic for hand reachability and multi-fingered inverse kinematics. The system consists of an online execution module and an offline optimization module. During execution the system plans and executes fingertip grasps using Canny's grasp quality metric and a learned random forest based hand reachability heuristic. In the offline module, this heuristic is improved based on a grasping manifold that is incrementally learned from the experiences collected during execution. The system is evaluated both in simulation and on a Schunk-SDH dexterous hand mounted on a KUKA-KR5 arm. We show that, as the grasping manifold is adapted to the system's experiences, the heuristic becomes more accurate, which results in an improved performance of the execution module. The improvement is not only observed for experienced objects, but also for previously unknown objects of similar sizes.

关键词： Evolution Fingertip Grasping

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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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