35 research outputs found

    Global Search with Bernoulli Alternation Kernel for Task-oriented Grasping Informed by Simulation

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    We develop an approach that benefits from large simulated datasets and takes full advantage of the limited online data that is most relevant. We propose a variant of Bayesian optimization that alternates between using informed and uninformed kernels. With this Bernoulli Alternation Kernel we ensure that discrepancies between simulation and reality do not hinder adapting robot control policies online. The proposed approach is applied to a challenging real-world problem of task-oriented grasping with novel objects. Our further contribution is a neural network architecture and training pipeline that use experience from grasping objects in simulation to learn grasp stability scores. We learn task scores from a labeled dataset with a convolutional network, which is used to construct an informed kernel for our variant of Bayesian optimization. Experiments on an ABB Yumi robot with real sensor data demonstrate success of our approach, despite the challenge of fulfilling task requirements and high uncertainty over physical properties of objects.Comment: To appear in 2nd Conference on Robot Learning (CoRL) 201

    Utilisation du théorème de Buckingham pour le transfert d'apprentissage multisystèmes : une étude de cas avec trois véhicules partageant la même base de données

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    Avec l’intérêt grandissant pour les algorithmes d’apprentissage, de nombreux efforts ont été déployés pour utiliser cette technologie pour des planificateurs et des contrôleurs de différents systèmes pour que ceux-ci puissent tirer des enseignements de leurs expériences et s’améliorer au fil du temps. Cependant, des difficultés majeures limitent le succès de ces modèles d’apprentissage lorsqu’il s’agit de contrôler des plateformes physiques réelles puisque cette méthode nécessite un nombre considérable de tests expérimentaux ou doit se baser sur des simulations à haute fidélité. Le projet de recherche dont traite ce mémoire explore le potentiel d’une architecture d’apprentissage qui exploite des nombres adimensionnels basés sur le théorème π de Buckingham afin d’accélérer et d’améliorer la précision de cet apprentissage et de faciliter le partage des connaissances entre des systèmes similaires pour pallier à ces différents problèmes. Ce mémoire présente une étude de cas utilisant trois véhicules de tailles réduites pour comparer les résultats de modèles d’apprentissage traditionnels avec les résultats obtenus grâce à la méthode adimensionnelle proposée. Le problème étudié est la prédiction de la position et de l’orientation relative finale d’un véhicule roulant à vitesse initiale vi après une manœuvre soudaine de changement de direction combiné au freinage sur différents types de chaussée. Cette prédiction pourrait alors être utilisée dans un pipeline de contrôle pour sélectionner la meilleure manœuvre dans une situation d’urgence. D’abord, la comparaison est faite grâce à des données recueillies à l’aide d’une simulation cinématique simplifiée. Ensuite, la méthode proposée est validée grâce à des valeurs expérimentales obtenues avec les trois plateformes robotiques étudiées. Les résultats en simulation montrent non seulement que cette nouvelle approche peut accélérer le taux d’apprentissage et améliorer la précision du modèle, mais aussi que l’apprentissage peut être transféré d’un système à l’autre plus aisément qu’avec un modèle d’apprentissage traditionnel. Les tests expérimentaux ont toutefois généré des résultats moins concluant dut à la complexité de capturer tous les phénomènes dynamiques reliés à une manœuvre d’urgence

    Using Buckingham's π\pi Theorem for Multi-System Learning Transfer: a Case-study with 3 Vehicles Sharing a Database

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    Learning schemes for planning and control are limited by the difficulty of collecting large amounts of experimental data or having to rely on high-fidelity simulations. This paper explores the potential of a proposed learning scheme that leverages dimensionless numbers based on Buckingham's π\pi theorem to improve data efficiency and facilitate knowledge sharing between similar systems. A case study using car-like robots compares traditional and dimensionless learning models on simulated and experimental data to validate the benefits of the new dimensionless learning approach. Preliminary results show that this new dimensionless approach could accelerate the learning rate and improve the accuracy of the model and should be investigated further

    Intersection-free Robot Manipulation with Soft-Rigid Coupled Incremental Potential Contact

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    This paper presents a novel simulation platform, ZeMa, designed for robotic manipulation tasks concerning soft objects. Such simulation ideally requires three properties: two-way soft-rigid coupling, intersection-free guarantees, and frictional contact modeling, with acceptable runtime suitable for deep and reinforcement learning tasks. Current simulators often satisfy only a subset of these needs, primarily focusing on distinct rigid-rigid or soft-soft interactions. The proposed ZeMa prioritizes physical accuracy and integrates the incremental potential contact method, offering unified dynamics simulation for both soft and rigid objects. It efficiently manages soft-rigid contact, operating 75x faster than baseline tools with similar methodologies like IPC-GraspSim. To demonstrate its applicability, we employ it for parallel grasp generation, penetrated grasp repair, and reinforcement learning for grasping, successfully transferring the trained RL policy to real-world scenarios

    Dexterous actuation

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    Methods that have been developed for actuation system evaluation are normally generic, and primarily intended to facilitate actuator selection. Here, we address specifically those engineering devices that exhibit multiple-degree-of-freedom motions under space and weight constraints, and focus on the evaluation of the total actuation solution. We suggest a new measure that we provisionally call ‘Actuation Dexterity’, which interrogates the effectiveness of this total solution and serves as a design support tool. The new concept is developed in the context of artificial hands, and the approach is based on the review and analysis of thirty-six different artificial hand projects described in the literature. We have identified forty-eight unique evaluation criteria that are relevant to the actuation of devices of this type, and have devised a scoring method that permits the quantification of the actuation dexterity of a given device. We have tested this approach by evaluating and quantifying the actuation dexterity of five different artificial hands from the literature. Finally, we discuss the implications of this approach to the design process, and the portability of the approach between different device types.peer-reviewe

    A Dynamic Manipulation Strategy for an Intervention Autonomous Underwater Vehicle

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    This paper presents the modelling and the control architecture of an Autonomous Underwater Vehicle for Intervention (I-AUV). Autonomous underwater manipulation with free-floating base is still an open topic of research, far from reaching an industrial product. Dynamic manipulation tasks, where relevant vehicle velocities are required during manipulation, over an additional challenge. In this paper, the accurate modelling of an I-AUV is described, not neglecting the interaction with the fluid. A grasp planning strategy is proposed and integrated in the control of the whole system. The performances of the I-AUV have been analysed by means of simulations of a dynamic manipulation task
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