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IEEE-RAS International Conference on Humanoid Robots

作者： Michael Bombile Aude Billard Learning Algorithms and Systems Laboratory EPFL Lausanne Switzerland

ISBN: (纸本)9781538646793

This paper proposes a capture-point based reactive omnidirectional controller for bipedal locomotion. The proposed scheme, formulated within Model Predictive Control (MPC) framework, exploits concurrently the Center of Mass (CoM) and Capture Point (CP) dynamics. It allows the on-line generation of the CoM reference trajectory and the automatic generation of footstep positions and orientations in response to a given velocity to be tracked, or a disturbance to be rejected by the robot while accounting explicitly for different walking constraints. For instance, in order to cope with disturbance such as a push, the proposed controller not only adjusts the position of the Center of Pressure (CoP) within the support foot, but can also induce at least one step with appropriate length allowing thus to maintain the stability of the robot. Finally, the proposed algorithm is validated through simulations and actual experiments on the humanoid robot iCub.

关键词： Legged locomotion Trajectory Stability analysis Collision avoidance Predictive models

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The life of iCub, a little humanoid robot learning from humans throug tactile sensing

The life of iCub, a little humanoid robot learning from huma...

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

作者： Eric Sauser Brenna Argall Aude Billard Learning Algorithms and Systems Laboratory EPF Lausanne Lausanne Switzerland

Nowadays, programming by demonstration (PbD) has become an important paradigm for policy learning in robotics [3]. The idea of having robots capable of learning from humans through natural communication means is indeed fascinating. As an extension of the traditional PbD learning scheme, where robots only learn by observing a human teacher, our work follows the recently suggested principle of policy refinement and reuse through interactive corrective feedback [1].

关键词： Robot sensing systems Abstracts

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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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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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Transferring Shotcrete Skills to Robots

Transferring Shotcrete Skills to Robots

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

作者： Rui Wu Soheil Gholami Tristan Bonato Louis Munier Aude Billard Learning algorithms and systems laboratory Swiss Federal Institute of Technology Lausanne Switzerland

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Traditionally performed by skilled nozzle opera-tors, shotcrete remains a painful and risky task, where recurrent physical discomfort leads to severe and long-term health issues. When performed by novices or ill-trained workers, it can lead to waste. The shotcrete process guidelines advise operators on how to best maneuver the nozzle tool during spraying to reduce waste. This technique involves moving the nozzle with rhythmic circular movements while remaining at an optimal distance from the shotcrete surface. Adhering to these standards is challenging and adds to the operator's discomfort. To alleviate these issues, robotic solutions can be developed to partially or completely substitute the operator's work. This paper presents a first step towards modeling the technique employed by experienced nozzle operators and transferring them to control a robotic arm. We recorded the motion generated at the nozzle during a set of shotcrete operations by two expert nozzle operators and one novice. We analyze the pattern of motion and confirm that we model the shotcrete task and showcase its use for autonomous control of a mock-up of a shotcrete robot using expandable foam. Furthermore, we implement an intuitive shared control framework to support operators during shotcrete. The experimental results from quasi-real-world evaluations of our proposed framework on a seven-degrees-of-freedom robotic manipulator demonstrate the efficacy of our proposed control approach.

关键词： Analytical models Spraying Biomechatronics Manipulators Surface treatment Standards Guidelines

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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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9th IEEE-RAS International Conference on Humanoid Robots, HUMANOIDS09

作者： Evrard, Paul Gribovskaya, Elena Calinon, Sylvain Billard, Aude Kheddar, Abderrahmane CNRS-UM2 LIRMM Montpellier France CNRS-AIST JRL UMI3218/CRT Tsukuba Japan Learning Algorithms and Systems Laboratory China Italian Institute of Technology Italy

ISBN: (纸本)9781424445882

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 followerlleader 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 HRP2;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. ©2009 IEEE.

关键词： Anthropomorphic robots

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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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Biologically Inspired Multimodal Integration: Interferences in a Human-Robot Interaction Game

Biologically Inspired Multimodal Integration: Interferences ...

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2006 IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Eric L. Sauser Aude G. Billard Learning Algorithms and Systems Laboratory Ecole Polytechnique Federale de Lausanne Lausanne Switzerland

This paper presents a biologically inspired approach to multimodal integration and decision-making in the context of human-robot interactions. More specifically, we address the principle of ideomotor compatibility by which observing the movements of others influences the quality of one's own performance. This fundamental human ability is likely to be linked with human imitation abilities, social interactions, the transfer of manual skills, and probably to mind reading. We present a robotic control model capable of integrating multimodal information, decision making, and replicating a stimulus-response compatibility task, originally designed to measure the effect of ideomotor compatibility on human behavior. The model consists of a neural network based on the dynamic field approach, which is known for its natural ability for stimulus enhancement as well as cooperative and competitive interactions within and across sensorimotor representations. Finally, we discuss how the capacity for ideomotor facilitation can provide the robot with human-like behavior, but at the expense of several disadvantages, such as hesitation and even mistakes

关键词： Interference Humans Robot sensing systems Decision making Robot control Humanoid robots Intelligent robots Game theory Laboratories Neural networks

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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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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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