The iCub Platform: A Tool for Studying Intrinsically Motivated Learning

Natale, Lorenzo; Nori, Francesco; Metta, Giorgio; Fumagalli, Matteo; Ivaldi, Serena; Pattacini, Ugo; Randazzo, Marco; Schmitz, Alexander; Sandini, Giulio

doi:10.1007/978-3-642-32375-1_17

Cited by 40 publications

(36 citation statements)

References 42 publications

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“…Robotics setup: The experiments were carried out with the humanoid iCub [14], a robot shaped like a 4 years old child. The robot was standing on a fixed pole and it was controlled by an operator hidden behind a wall.…”

Section: Methodsmentioning

confidence: 99%

Predicting Extraversion from Non-verbal Features During a Face-to-Face Human-Robot Interaction

Rahbar

Anzalone²,

Varni

et al. 2015

Social Robotics

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Abstract. In this paper we present a system for automatic prediction of extraversion during the first thin slices of human-robot interaction (HRI). This work is based on the hypothesis that personality traits and attitude towards robot appear in the behavioural response of humans during HRI. We propose a set of four non-verbal movement features that characterize human behavior during interaction. We focus our study on predicting Extraversion using these features, extracted from a dataset consisting of 39 healthy adults interacting with the humanoid iCub. Our analysis shows that it is possible to predict to a good level (64%) the Extraversion of a human from a thin slice of interaction relying only on non-verbal movement features. Our results are comparable to the state-of-the-art obtained in HHI [23].

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Section: Methodsmentioning

confidence: 99%

Predicting Extraversion from Non-verbal Features During a Face-to-Face Human-Robot Interaction

Rahbar

Anzalone²,

Varni

et al. 2015

Social Robotics

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“…In this case, the investigated system for replication is part of the example applications distributed with the iCub software [14]. This application is meant to help new iCub users approach the software.…”

Section: B Icubmentioning

confidence: 99%

Towards automated system and experiment reproduction in robotics

Lier

Hanheide

Natale

et al. 2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-Even though research on autonomous robots and human-robot interaction accomplished great progress in recent years, and reusable soft-and hardware components are available, many of the reported findings are only hardly reproducible by fellow scientists. Usually, reproducibility is impeded because required information, such as the specification of software versions and their configuration, required data sets, and experiment protocols are not mentioned or referenced in most publications. In order to address these issues, we recently introduced an integrated tool chain and its underlying development process to facilitate reproducibility in robotics. In this contribution we instantiate the complete tool chain in a unique user study in order to assess its applicability and usability. To this end, we chose three different robotic systems from independent institutions and modeled them in our tool chain, including three exemplary experiments. Subsequently, we asked twelve researchers to reproduce one of the formerly unknown systems and the associated experiment. We show that all twelve scientists were able to replicate a formerly unknown robotics experiment using our tool chain.

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“…To address a task similar to the previous one, we perform a task involving digits. We use the iCub humanoid robot [62] to collect a new dataset. We record the joint velocities of the six degrees of freedom of the right arm while it is moved by a human operator in a writing task.…”

Section: Mixing Visual Input With Proprioceptionmentioning

confidence: 99%

“…Following the manifold hypothesis for classification [61], this architecture learns to identify and to represent different sub-manifolds in a high dimensional input. We show the effectiveness of our solution on a multimodal dataset acquired on the humanoid robot iCub [62].…”

Section: Introductionmentioning

confidence: 99%

Deep unsupervised network for multimodal perception, representation and classification

Droniou

Ivaldi

Sigaud

2015

Robotics and Autonomous Systems

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In this paper, we tackle the problem of multimodal learning for autonomous robots. Autonomous robots interacting with humans in an evolving environment need the ability to acquire knowledge from their multiple perceptual channels in an unsupervised way. Most of the approaches in the literature exploit engineered methods to process each perceptual modality. In contrast, robots should be able to acquire their own features from the raw sensors, leveraging the information elicited by interaction with their environment: learning from their sensorimotor experience would result in a more efficient strategy in a life-long perspective. To this end, we propose an architecture based on deep networks, which is used by the humanoid robot iCub to learn a task from multiple perceptual modalities (proprioception, vision, audition). By structuring high-dimensional, multimodal information into a set of distinct sub-manifolds in a fully unsupervised way, it performs a substantial dimensionality reduction by providing both a symbolic representation of data and a fine discrimination between two similar stimuli. Moreover, the proposed network is able to exploit multimodal correlations to improve the representation of each modality alone.

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The iCub Platform: A Tool for Studying Intrinsically Motivated Learning

Cited by 40 publications

References 42 publications

Predicting Extraversion from Non-verbal Features During a Face-to-Face Human-Robot Interaction

Predicting Extraversion from Non-verbal Features During a Face-to-Face Human-Robot Interaction

Towards automated system and experiment reproduction in robotics

Deep unsupervised network for multimodal perception, representation and classification

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