Bimanual Skill Learning with Pose and Joint Space Constraints

Sylvain Calinon
2018
Article de conférence

As humanoid robots become commonplace, learning and control algorithms must take into account the new challenges imposed by this morphology, if we aim to fully exploit their potential. One of the most prominent characteristics of such robots is their bimanual structure. Most research on learning bimanual skills has focused on the coordination between end-effectors, exploiting operational space formulations. However, motion patterns in bimanual scenarios are not exclusive to operational space, also occurring at joint level. Moreover, in addition to position, the end-effector orientation is also essential for bimanual operation. Here, we propose a framework for simultaneously learning constraints in configuration and operational spaces, while considering end-effector orientations, overlooked in previous works. In particular, we extend the Task-Parameterized Gaussian Mixture Model (TP-GMM) with novel, Jacobian-based operators that address the foregoing problem. The proposed framework is evaluated in a bimanual task with the COMAN humanoid that requires the consideration of operational and configuration space movements.

À propos de ce résultat

Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.

Concepts associés

Chargement

Publications associées

Chargement

Sylvain Calinon
2018
Article de conférence

Résumé

Official source

https://infoscience.epfl.ch/record/263152?ln=fr

À propos de ce résultat

Concepts associés (18)

Concepts associés

Chargement

Publications associées (123)

Publications associées

Chargement

Robot

vignette|Atlas (2013), robot androïde de Boston Dynamics vignette|Bras manipulateurs dans un laboratoire (2009) vignette|NAO (2006), robot humanoïde éducatif d'Aldebaran Robotics vignette|DER1 (2005),

Robotique

thumb|upright=1.5|Nao, un robot humanoïde. thumb|upright=1.5|Des robots industriels au travail dans une usine. La robotique est l'ensemble des techniques permettant la conception et la réalisation de

Apprentissage

L’apprentissage est un ensemble de mécanismes menant à l'acquisition de savoir-faire, de savoirs ou de connaissances. L'acteur de l'apprentissage est appelé apprenant. On peut opposer l'apprentissag

Handling of multiple constraints and motion alternatives in a robot programming by demonstration framework

Aude Billard, Sylvain Calinon

We consider the problem of learning robust models of robot motion through demonstration. An approach based on Hidden Markov Model (HMM) and Gaussian Mixture Regression (GMR) is proposed to extract redundancies across multiple demonstrations, and build a time- independent model of a set of movements demonstrated by a human user. Two experiments are presented to validate the method, that consist of learning to hit a ball with a robotic arm, and of teaching a humanoid robot to manipulate a spoon to feed another humanoid. The experiments demonstrate that the proposed model can efficiently handle several aspects of learning by imitation. We first show that it can be utilized in an unsupervised learning manner, where the robot is autonomously organizing and encoding variants of motion from the multiple demonstrations. We then show that the approach allows to robustly generalize the observed skill by taking into account multiple constraints in task space during reproduction.

2009

Chargement

Learning Task Priorities from Demonstrations

Sylvain Calinon

As humanoid robots become increasingly popular, learning and control algorithms must take into account the new constraints and challenges inherent to these platforms, if we aim to fully exploit their potential. One of the most prominent of such aspects is their bimanual structure. Most research on learning bimanual skills has focused on the coordination between end-effectors, exploiting operational space formulations. However, motion patterns in bimanual scenarios are not exclusive to operational space, also occurring at the joint level. Moreover, bimanual operation offers the possibility to carry out more than one manipulation task at the same time, which in turn introduces the problem of task prioritization. Here we address the aforementioned problems from a robot Learning from Demonstration perspective. In particular, we present an extension of the Task-Parameterized Gaussian Mixture Model (TP-GMM) employing operators that allow for tackling such problems. The presented approach – despite the focus on bimanual operation – can be applied in any scenario where the prioritization between potentially conflicting tasks needs to be learned. We evaluate the proposed framework in: (i) two different bimanual tasks with the COMAN and WALK-MAN humanoids that either require the consideration of operational and configuration space movements or the prioritization of tasks and (ii) a loco-manipulation scenario with the Centauro robot in simulation, where the priority of the floating base position needs to be learned, showing that the approach can be exploited in generic task prioritization scenarios.

2019

Chargement

, ,

In this paper we combine kinesthetic demonstrations and dynamical systems to enable a humanoid robot to imitate constrained reaching gestures directed toward a target. Using a learning algorithm based on Gaussian Mixture Regression, the task constraints are extracted from several demonstrations. Those constraints take the form of desired velocity profiles of the end-effector and joint angle variables. The velocity profiles are then used to modulate a dynamical system which has the reaching target as attractor. This way, the reaching trajectory can be reshaped in order to satisfy the constraints of the task, while preserving the adaptability and robustness provided by the dynamical system. In particular, the system can adapt to changes in the initial conditions and to target displacements occurring during the movement execution. We first evaluate the potential of this method on experiments involving the Hoap3 humanoid robot putting an object into a box. We then show how a manipulation tasks can be executed as sequences of such constrained reaching movement. This is illustrated on a packaging task performed by the robot.

2006

Chargement