A framework integrating statistical and social cues to teach a humanoid robot new skills

Aude Billard, Sylvain Calinon
2008
Article de conférence

Bringing robots as collaborative partners into homes presents various challenges to human-robot interaction. Robots will need to interact with untrained users in environments that are originally designed for humans. Compared to their industrial homologous form, humanoid robots can not be preprogrammed with an initial set of behaviours. They should adapt their skills to a huge range of possible tasks without needing to change the environments and tools to fit their needs. The rise of these humanoids implies an inherent social dimension to this technology, where the end-users should be able to teach new skills to these robots in an intuitive manner, relying only on their experience in teaching new skills to other human partners. Our research aims at designing a generic Robot Programming by Demonstration (RPD) framework based on a probabilistic representation of the task constraints, which allows to integrate information from cross-situational statistics and from various social cues such as joint attention or vocal intonation. This paper presents our ongoing research towards bringing user- friendly human-robot teaching systems that would speed up the skill transfer process.

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Aude Billard, Sylvain Calinon
2008
Article de conférence

Résumé

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https://infoscience.epfl.ch/record/117850?ln=fr

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Publications associées (48)

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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

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

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We introduce a new multimodal interaction dataset with extensive annotations in a conversational Human-Robot-Interaction (HRI) scenario. It has been recorded and annotated to benchmark many relevant perceptual tasks, towards enabling a robot to converse with multiple humans, such as speaker localization, key word spotting, speech recognition in audio domain; tracking, pose estimation, nodding, visual focus of attention estimation in visual domain; and an audio-visual task such as addressee detection. Some of the above mentioned tasks could benefit from information sensed from several modalities and recorded states of the robot. As compared to recordings done with a static camera, this corpus involves the head-movement of a humanoid robot (due to gaze change, nodding), making it challenging for tracking. Also, the significant background noise present in a real HRI setting makes tasks in the auditory domain more challenging. From the interaction point of view, our scenario, where the robot explains paintings in a room and then quizzes the participants, allows to analyze the quality of the interaction and the behavior of the human interaction partners.

2012

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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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Stochastic Gesture Production and Recognition Model for a Humanoid Robot

Aude Billard, Sylvain Calinon

Robot Programming by Demonstration (PbD) aims at developing adaptive and robust controllers to enable the robot to learn new skills by observing and imitating a human demonstration. While the vast majority of PbD works focused on systems that learn a specific subset of tasks, our work explores the problem of recognition, generalization, and reproduction of tasks in a unified mathematical framework. The approach makes abstraction of the task and dataset at hand to tackle the general issue of learning which of the features are the relevant ones to imitate. In this paper, we present an implementation of this framework to the determination of the optimal strategy to reproduce arbitrary gestures. The model is tested and validated on a humanoid robot, using recordings of the kinematics of the demonstrator's arm motion. The hand path and joint angle trajectories are encoded in Hidden Markov Models. The system uses the optimal prediction of the models to generate the reproduction of the motion.

2004

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