Beaming the Gaze of a Humanoid Robot

Bailly, Gérard; Elisei, Frédéric; Sauze, Miquel

doi:10.1145/2701973.2701992

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…We analyzed here the very first interaction data we collected on face-to-face human-human interviews with a unique professional whose communication skills we have the ambition to endow social robots with. We envision filling the gap between human-human and human-robot interactions with immersive teleoperation (see Figure 10 and [23] [24]) where the communication is mediated by a robotic embodiment that inherently provides perception and action limitations to the higher-level cognitive processes of the pilot. We will now explore the impact of such a robotic embodiment on the behaviors we have characterized here.…”

Section: Discussionmentioning

confidence: 99%

Quantitative Analysis of Backchannels Uttered by an Interviewer During Neuropsychological Tests

Bailly¹,

Elisei²,

Juphard³

et al. 2016

Interspeech 2016

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This paper examines in detail the backchannels uttered by a French professional interviewer during a neuropsychological test of verbal memories. These backchannels are short utterances such as oui, d'accord, uhm, etc. They are mainly produced here to encourage subjects to retrieve a set of words after their controlled encoding. We show that the choice of lexical items, their production rates and their associated prosodic contours are influenced by the subject performance and conditioned by the protocol.

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Section: Discussionmentioning

confidence: 99%

Quantitative Analysis of Backchannels Uttered by an Interviewer During Neuropsychological Tests

Bailly¹,

Elisei²,

Juphard³

et al. 2016

Interspeech 2016

Self Cite

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“…As demonstrated in the figure 1, the HRI can be viewed as a planning problem: in an initial run of the interaction, the robot speech acts are governed by observation, imitation or demonstration techniques [2,21]. One particular approach that seems promising is that of 'beaming' by human pilots [3]. Thanks to this technique, a human operator can perceive, analyze, act and interact with a remote person through robotic embodiment.…”

Section: Problem Statementmentioning

confidence: 99%

A review of learning planning action models

Arora¹,

Fiorino²,

Pellier³

et al. 2018

The Knowledge Engineering Review

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For social robots to be brought more into widespread use in the fields of companionship, care taking and domestic help, they must be capable of demonstrating social intelligence. In order to be acceptable, they must exhibit socio-communicative skills. Classic approaches to program HRI from observed human-human interactions fails to capture the subtlety of multimodal interactions as well as the key structural differences between robots and humans. The former arises due to a difficulty in quantifying and coding multimodal behaviours, while the latter due to a difference of the degrees of liberty between a robot and a human. However, the notion of reverse engineering from multimodal HRI traces to learn the underlying behavioral blueprint of the robot given multimodal traces seems an option worth exploring. With this spirit, the entire HRI can be seen as a sequence of exchanges of speech acts between the robot and human, each act treated as an action, bearing in mind that the entire sequence is goal-driven. Thus, this entire interaction can be treated as a sequence of actions propelling the interaction from its initial to goal state, also known as a plan in the domain of AI planning. In the same domain, this action sequence that stems from plan execution can be represented as a trace. AI techniques, such as machine learning, can be used to learn behavioral models (also known as symbolic action models in AI), intended to be reusable for AI planning, from the aforementioned multimodal traces. This article reviews recent machine learning techniques for learning planning action models which can be applied to the field of HRI with the intent of rendering robots as socio-communicative.

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Acquiring Human-Robot Interaction skills with Transfer Learning Techniques

Mohammed

Bailly

Pellier

2017

Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction

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International audienceRecently, an increasing interest in the research commu nity is how to enable robots to interact and communicate with humans, which is a major step towards integrating robots in our daily life. Within the large domain of Human- Robot Interaction (HRI), this research area is called Social robotics. Communicating with humans is a complex process, involving several modalities such as speech, facial expressions, gaze, head movements, hand gestures, etc. Social robotics focuses on modeling how humans use these communication channels in order to smoothly interact with each other, and how to endow a robot with similar skills. Modeling such complex multimodal in- teractions has proven to be a challenge.Machine learning is invading this domain. Recent research performed on interactive data (i.e. challenging the problem of generating co-verbal gestures of one participant given the co-verbal gestures of the other and verbal activities of both participants) showed a great potential for machine learning in learning complex HRI behaviors. However, the behavioural models are task- and situation-specific: if anything changes in the learning problem (the input distribution or shape, the interaction scenario, etc), we need to learn everything again from scratch. This means that policies learnt in one environment might not work – or work sub-optimally – in other environments and conditions.A growing interest of research in machine learning is on how to transfer knowledge learned from one task (source task) to a new task (target task) . The idea is to capture the similarities between the source and the target tasks, and exploit them in order to learn the tar- get task faster. The shared knowledge can be implicit, like finding latent variables for the input distributions of both tasks [Pan et al. 2011]. It can also be explicit, like representing robotic knowledge as skills.Our challenge is thus to develop methods to transfer the knowledge model the robot learns on one interactive situation with humans to new tasks/situations - notably to new interactive tasks -, in order to enable rapid learning and adaptation to these new tasks

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Beaming the Gaze of a Humanoid Robot

Cited by 4 publications

References 5 publications

Quantitative Analysis of Backchannels Uttered by an Interviewer During Neuropsychological Tests

Quantitative Analysis of Backchannels Uttered by an Interviewer During Neuropsychological Tests

A review of learning planning action models

Acquiring Human-Robot Interaction skills with Transfer Learning Techniques

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