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

,
2009
Article de conférence

Résumé

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.

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,
2009
Article de conférence

Résumé

Official source

À propos de ce résultat

Concepts associés (18)

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),

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

Un modèle de Markov caché (MMC, terme et définition normalisés par l’ISO/CÉI [ISO/IEC 2382-29:1999]) — (HMM)—, ou plus correctement (mais non employé) automate de Markov à états cachés, est un modèl

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Learning of Gestures by Imitation in a Humanoid Robot

Aude Billard, Sylvain Calinon

In this chapter, we explore the issue of encoding, recognizing, generalizing and reproducing arbitrary gestures. We address one major and generic issue, namely how to discover the essence of a gesture, i.e. how to find a representation of the data that encapsulates only the key aspects of the gesture, and discards the intrinsic variability across people motions. The model is tested and validated in a humanoid robot, using kinematics data of human motion. In order for the robot to learn new skills by imitation, it must be endowed with the ability to generalize over multiple demonstrations. To achieve this, the robot must encode multivariate time-dependent data in an efficient way. Principal Component Analysis and Hidden Markov Models are used to reduce the dimensionality of the dataset and to extract the primitives of the motion. The model takes inspiration in a recent trend of research that aims at defining a formal mathematical framework for imitation learning. In particular, it stresses the fact that the observed elements of a demonstration, and the organization of these elements should be stochastically described to have a robust robotic application. It bears similarities with theoretical models of animal imitation, and offers at the same time a probabilistic description of the data, more suitable for a real-world application.

Cambridge University Press2007

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Robot Programming by Demonstration (RbD), also referred to as Learning by Imitation, explores user-friendly means of teaching a robot new skills. Recent advances in RbD have identified a number of key-issues for ensuring a generic approach to the transfer of skills across various agents and contexts. This thesis focuses on the two generic questions of what-to-imitate and how-to-imitate, which are respectively concerned with the problem of extracting the essential features of a task and determining a way to reproduce these essential features in different situations. The perspective adopted in this work is that a skill can be described efficiently at a trajectory level and that the robot may infer what are the important characteristics of the skill by observing multiple demonstrations of it, assuming that the important characteristics are invariant across the demonstrations. The proposed approach is based on the use of well-established statistical methods in a RbD application, by using Hidden Markov Model (HMM) as a first approach and then moving on to the joint use of Gaussian Mixture Model (GMM) and Gaussian Mixture Regression (GMR). Even if these methods were applied extensively in various fields of research including human motion analysis and robotics, their use essentially focused on gesture recognition rather than on gesture reproduction. Moreover, the models were usually trained offline using large datasets. Thus, the use and combination of these machine learning techniques in a Learning by Imitation framework is challenging and has not been extensively studied yet. In this thesis, we show that these methods are well suited for incremental and active teaching scenarios where the user only provides a small number of demonstrations, either by wearing a set of motions sensors attached to his/her body, or by helping the robot refine its skill by kinesthetic teaching, that is, by embodying the robot and putting it through the motion. These techniques are then applied for enabling a Fujitsu HOAP-2 and HOAP-3 humanoid robot to extract automatically different constraints by observing several manipulation skills and to reproduce these skills in various situations through Gaussian Mixture Regression (GMR). The contributions of this thesis are threefold: (1) it contributes to RbD by proposing a generic probabilistic framework to deal with recognition, generalization, reproduction and evaluation issues, and more specifically to deal with the automatic extraction of task constraints and with the determination of a controller satisfying several constraints simultaneously to reproduce the skill in a different context; (2) it contributes to Human-Robot Interaction (HRI) by proposing active teaching methods that puts the human teacher "in the loop" of the robot's learning by using an incremental scaffolding process and by using different modalities to produce the demonstrations; (3) it finally contributes to robotics and HRI through various real-world applications of the framework showing learning and reproduction of communicative gestures and manipulation skills. The generality of the proposed framework is also demonstrated through its joint use with other learning approaches in collaborative experiments.

EPFL2007

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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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EPFL2007