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8th Conference on Robot Learning, CoRL 2024

作者： Mitchell, Alexander L. Merkt, Wolfgang Papatheodorou, Aristotelis Havoutis, Ioannis Posner, Ingmar Applied AI Lab United States Dynamic Robot Systems Control University of Oxford United Kingdom

The current state-of-the-art in quadruped locomotion is able to produce a variety of complex motions. These methods either rely on switching between a discrete set of skills or learn a distribution across gaits using complex black-box models. Alternatively, we present Gaitor, which learns a disentangled and 2D representation across locomotion gaits. This learnt representation forms a planning space for closed-loop control delivering continuous gait transitions and perceptive terrain traversal. Gaitor's latent space is readily interpretable and we discover that during gait transitions, novel unseen gaits emerge. The latent space is disentangled with respect to footswing heights and lengths. This means that these gait characteristics can be varied independently in the 2D latent representation. Together with a simple terrain encoding and a learnt planner operating in the latent space, Gaitor can take motion commands including desired gait type and swing characteristics all while reacting to uneven terrain. We evaluate Gaitor in both simulation and the real world on the ANYmal C platform. To the best of our knowledge, this is the first work learning a unified and interpretable latent space for multiple gaits, resulting in continuous blending between different locomotion modes on a real quadruped robot. An overview of the methods and results in this paper is found at https://***/eVFQbRyilCA. © 2024 Proceedings of Machine Learning Research.

关键词： Closed loop control systems

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Nonparametric Anisotropy-Based Bounded Real Lemma for Polytopic Descriptor systems 5

Nonparametric Anisotropy-Based Bounded Real Lemma for Polyto...

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5th International Conference on control systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2023

作者： Belov, Alexey A. Andrianova, Olga G. V.A. Trapeznikov Institute of Control Sciences Russian Academy of Sciences Lab. of Dynamic Control Systems Moscow Russia

ISBN: (纸本)9798350309126

In this paper, robust anisotropy-based analysis problem for discrete-time descriptor systems with polytopic uncertainties is stated and solved. Sufficient conditions for anisotropic norm boundedness are formulated in terms of linear matrix inequalities. New conditions, being nonparametric ones, are numerically effective, that is illustrated by numerical example. © 2023 IEEE.

关键词： Anisotropy

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New Results on Anisotropy-Based Analysis and State-Feedback control of Uncertain systems 5

New Results on Anisotropy-Based Analysis and State-Feedback ...

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5th International Conference on control systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2023

作者： Belov, Alexey A. Russian Academy of Sciences Lab. Dynamic Control Systems V. A. Trapeznikov Institute of Control Sciences Moscow Russia

This paper presents convex solution to the anisotropy-based analysis and state-feedback control of linear discrete-time systems with norm bounded uncertainties. The solution is obtained using linear matrix inequalitie... 详细信息

ISBN: (纸本)9798350309126

关键词： Anisotropy

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Autonomous Search of Real-Life Environments Combining dynamical System-Based Path Planning and Unsupervised Learning

SSRN

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SSRN 2024年

作者： Amadasun, Uyiosa Philip Nili Ahmadabadi, Zahra McNamee, Patrick Naseradinmousavi, Peiman Dynamic Systems and Intelligent Machines Lab Dynamic Systems and Control Lab San Diego State University Mechanical Engineering 5500 Campanile Drive San DiegoCA92182 United States

In recent years, advancements have been made towards the goal of using chaotic coverage path planners for autonomous search and traversal of spaces with limited environmental cues. However, the state of this field is still in its infancy as there has been little experimental work done. The existing experimental works have not developed robust methods to satisfactorily address the immediate set of problems a chaotic coverage path planner needs to overcome in order to scan realistic environments within reasonable coverage times. These immediate problems are as follows: (1) an obstacle avoidance technique which generally maintains the kinematic efficiency of the robot's motion, (2) a means to spread chaotic trajectories across the environment (especially crucial for large and/or complex-shaped environments) that need to be covered, and (3) a real-time coverage calculation technique that is accurate and independent of cell size. This paper aims to progress the field by proposing algorithms that address all of these problems by providing techniques for obstacle avoidance, chaotic trajectory dispersal, and accurate coverage calculation. The algorithms produce generally smooth chaotic trajectories and provide high scanning coverage of environments. These algorithms were created within the ROS framework and make up a newly developed chaotic path planning application. The performance of this application was comparable to that of a conventional optimal pathplanner. The performance tests were carried out in environments of various sizes, shapes, and obstacle densities, both in real-life and Gazebo simulations. © 2024, The Authors. All rights reserved.

关键词： Trajectories

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Gaitor: Learning a Unified Representation Across Gaits for Real-World Quadruped Locomotion

arXiv

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arXiv 2024年

作者： Mitchell, Alexander L. Merkt, Wolfgang Papatheodorou, Aristotelis Havoutis, Ioannis Posner, Ingmar Applied AI Lab University of Oxford United Kingdom Dynamic Robot Systems Control University of Oxford United Kingdom

The current state-of-the-art in quadruped locomotion is able to produce a variety of complex motions. These methods either rely on switching between a discrete set of skills or learn a distribution across gaits using complex black-box models. Alternatively, we present Gaitor, which learns a disentangled and 2D representation across locomotion gaits. This learnt representation forms a planning space for closed-loop control delivering continuous gait transitions and perceptive terrain traversal. Gaitor’s latent space is readily interpretable and we discover that during gait transitions, novel unseen gaits emerge. The latent space is disentangled with respect to footswing heights and lengths. This means that these gait characteristics can be varied independently in the 2D latent representation. Together with a simple terrain encoding and a learnt planner operating in the latent space, Gaitor can take motion commands including desired gait type and swing characteristics all while reacting to uneven terrain. We evaluate Gaitor in both simulation and the real world on the ANYmal C platform. To the best of our knowledge, this is the first work learning a unified and interpretable latent space for multiple gaits, resulting in continuous blending between different locomotion modes on a real quadruped robot. An overview of the methods and results in this paper is found at https://***/eVFQbRyilCA. Copyright © 2024, The Authors. All rights reserved.

关键词： Multipurpose robots

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Nonparametric Anisotropy-Based Bounded Real Lemma for Polytopic Descriptor systems

Nonparametric Anisotropy-Based Bounded Real Lemma for Polyto...

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International Conference on control systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Alexey A. Belov Olga G. Andrianova Lab. of Dynamic Control Systems V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia

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New Results on Anisotropy-Based Analysis and State-Feedback control of Uncertain systems

New Results on Anisotropy-Based Analysis and State-Feedback ...

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International Conference on control systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Alexey A. Belov Lab. Dynamic Control Systems V. A. Trapeznikov Institute of Control Sciences Russian Academy of Sciences Moscow Russian Federation

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Bayesian Multi-Task Learning MPC for Robotic Mobile Manipulation

arXiv

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arXiv 2022年

作者： Arcari, Elena Minniti, Maria Vittoria Scampicchio, Anna Carron, Andrea Farshidian, Farbod Hutter, Marco Zeilinger, Melanie N. Institute for Dynamic Systems and Control ETH Zurich Switzerland Robotic Systems Lab ETH Zurich Switzerland

Mobile manipulation in robotics is challenging due to the need of solving many diverse tasks, such as opening a door or picking-and-placing an object. Typically, a basic first-principles system description of the robot is available, thus motivating the use of model-based controllers. However, the robot dynamics and its interaction with an object are affected by uncertainty, limiting the controller’s performance. To tackle this problem, we propose a Bayesian multi-task learning model that uses trigonometric basis functions to identify the error in the dynamics. In this way, data from different but related tasks can be leveraged to provide a descriptive error model that can be efficiently updated online for new, unseen tasks. We combine this learning scheme with a model predictive controller, and extensively test the effectiveness of the proposed approach, including comparisons with available baseline controllers. We present simulation tests with a ball-balancing robot, and door-opening hardware experiments with a quadrupedal manipulator. Copyright © 2022, The Authors. All rights reserved.

关键词： controllers

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Unobtrusive Sleep Position Classification Using a Novel Optical Tactile Sensor

Unobtrusive Sleep Position Classification Using a Novel Opti...

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

作者： Alexander Breuss Carmelo Sferrazza Jonas Pleisch Raffaello D’Andrea Robert Riener Sensory-Motor Systems Lab Institute of Robotics and Intelligent Systems ETH Zurich Switzerland Institute for Dynamic Systems and Control ETH Zurich Switzerland Spinal Cord Injury Center University Hospital Balgrist Zurich Switzerland

Unobtrusive sleep position classification is essential for sleep monitoring and closed-loop intervention systems that initiate position changes. In this paper, we present a novel unobtrusive under-mattress optical tactile sensor for sleep position classification. The sensor uses a camera to track particles embedded in a soft silicone layer, inferring the deformation of the silicone and therefore providing information about the pressure and shear distributions applied to its *** characterized the sensitivity of the sensor after placing it under a conventional mattress and applying different weights (258 g, 500 g, 5000 g) on top of the mattress in various predefined locations. Moreover, we collected multiple recordings from a person lying in supine, lateral left, lateral right, and prone positions. As a proof-of-concept, we trained a neural network based on convolutional layers and residual blocks that classified the lying positions based on the images from the tactile *** observed a high sensitivity of the optical tactile sensor: Even after placing the sensor below a conventional mattress, we were able to detect our lowest test weight of 258 g. Using the neural network, we were able to classify the four sleep positions, lateral left, lateral right, prone, and supine with a classification accuracy of 91.2 %.The high sensitivity of the sensor, as well as the good performance in the classification task, demonstrate the feasibility of using such a sensor in a robotic bed *** Relevance— Positional Obstructive Sleep Apnea is highly prevalent across the general population. Today’s gold standard treatment of using CPAP ventilation is often not accepted, leading to unwanted treatment cessations. Alternative treatments, such as positional interventions through robotic beds are highly promising. However, these beds require reliable detection of the lying position. In this paper, we present a novel, scalable and completely unobtrusive sensor that is conce

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Longitudinal control of a Tilt-rotor VTOL UAV using Incremental Nonlinear dynamic Inversion

Longitudinal Control of a Tilt-rotor VTOL UAV using Incremen...

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International Conference on control, Automation and Diagnosis (ICCAD)

作者： Marco Schlatter Guillaume Ducard David Rohr Christopher Onder IDSC(IDSC = Institute for Dynamic Systems and Control) ETH Zurich Zurich Switzerland I3S Laboratory Univ. Côte d’Azur Sophia Antipolis France Autonomous Systems Lab ETH Zurich Zurich Switzerland

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

This publication presents a longitudinal incremental nonlinear dynamic inversion (INDI) control approach for a tilt-rotor aircraft. This control scheme is valid in all relevant flight states and thus does not require switching between different controllers or utilizing scheduled variables. The control approach is demonstrated in a simulation environment, showing that it is capable of performing transitions from hover to fixed-wing flight and vice versa while following a given velocity and altitude trajectory.

关键词： Automation Switches control systems Autonomous aerial vehicles Nonlinear dynamical systems Trajectory Aircraft

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