Motion autonomy for humanoids: experiments on HRP-2 No. 14

This paper deals with whole-body motion planning and dynamic control for humanoid from two aspects: locomotion including manipulation and reaching. In the first part, we address a problem of simultaneous locomotion and manipulation planning that combines a geometric and kinematic motion planner with a dynamic humanoid motion generator. The second part deals with whole-body reaching tasks by using a generalized inverse kinematics (IK) method to fully exploit the high redundancy of the humanoid robot. Through experiments using humanoid platform HRP-2 No. 14 installed at LAAS-CNRS, we first verify the validity of each method. An integrated experiment is then presented that unifies the both results via visual perception to execute an object-fetching task. Copyright (C) 2009 John Wiley & Sons, Ltd.

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2009
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https://infoscience.epfl.ch/record/159230?ln=fr

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Concepts associés (4)

Concepts associés

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Publications associées (4)

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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

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Bimanual Skill Learning with Pose and Joint Space Constraints

Sylvain Calinon

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.

2018

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Humanoïde

Le terme humanoïde signifie . Il évoque la bipédie, la présence de deux bras et d'une tête. Il s'agit donc uniquement d'un critère phénotypique et plus précisément morphologique. Terminologie

Planification de mouvement

La planification de mouvement (en anglais motion planning) est un ensemble de techniques mathématiques et informatiques permettant de calculer des trajectoires pour un système cinématique, avec pour

Humanoid robot

A humanoid robot is a robot resembling the human body in shape. The design may be for functional purposes, such as interacting with human tools and environments, for experimental purposes, such as t