61 citations · 218 across the 23 of their papers we have counts for
25 papers
SLOT-V: Supervised Learning of Observer Models for Legible Robot Motion Planning in Manipulation
Sebastian Wallkotter, Mohamed Chetouani, Ginevra Castellano
We present SLOT-V, a novel supervised learning framework that learns observer models (human preferences) from robot motion trajectories in a legibility context. Legibility measures…
Automatic Context-Driven Inference of Engagement in HMI: A Survey
Hanan Salam, Oya Celiktutan, Hatice Gunes +1
An integral part of seamless human-human communication is engagement, the process by which two or more participants establish, maintain, and end their perceived connection. Therefo…
How unitizing affects annotation of cohesion
Eleonora Ceccaldi, Nale Lehmann-Willenbrock, Erica Volta +3
This paper investigates how unitizing affects external observers' annotation of group cohesion. We compared unitizing techniques belonging to these categories: interval coding, con…
Overcoming Referential Ambiguity in Language-Guided Goal-Conditioned Reinforcement Learning
Hugo Caselles-Dupré, Olivier Sigaud, Mohamed Chetouani
Teaching an agent to perform new tasks using natural language can easily be hindered by ambiguities in interpretation. When a teacher provides an instruction to a learner about an…
Pragmatically Learning from Pedagogical Demonstrations in Multi-Goal Environments
Hugo Caselles-Dupré, Olivier Sigaud, Mohamed Chetouani
Learning from demonstration methods usually leverage close to optimal demonstrations to accelerate training. By contrast, when demonstrating a task, human teachers deviate from opt…
Two ways to make your robot proactive: reasoning about human intentions, or reasoning about possible futures
Sera Buyukgoz, Jasmin Grosinger, Mohamed Chetouani +1
Robots sharing their space with humans need to be proactive in order to be helpful. Proactive robots are able to act on their own initiative in an anticipatory way to benefit human…
A New Nonlinear speaker parameterization algorithm for speaker identification
Mohamed Chetouani, Marcos Faundez-Zanuy, Bruno Gas +1
In this paper we propose a new parameterization algorithm based on nonlinear prediction, which is an extension of the classical LPC parameters. The parameters performances are esti…
Pedagogical Demonstrations and Pragmatic Learning in Artificial Tutor-Learner Interactions
Hugo Caselles-Dupré, Mohamed Chetouani, Olivier Sigaud
When demonstrating a task, human tutors pedagogically modify their behavior by either "showing" the task rather than just "doing" it (exaggerating on relevant parts of the demonstr…
Learning Collective Action under Risk Diversity
Ramona Merhej, Fernando P. Santos, Francisco S. Melo +2
Collective risk dilemmas (CRDs) are a class of n-player games that represent societal challenges where groups need to coordinate to avoid the risk of a disastrous outcome. Multi-ag…
A new approach to evaluating legibility: Comparing legibility frameworks using framework-independent robot motion trajectories
Sebastian Wallkotter, Mohamed Chetouani, Ginevra Castellano
Robots that share an environment with humans may communicate their intent using a variety of different channels. Movement is one of these channels and, particularly in manipulation…
Does the Goal Matter? Emotion Recognition Tasks Can Change the Social Value of Facial Mimicry towards Artificial Agents
Giulia Perugia, Maike Paetzel-Prüssman, Isabelle Hupont +4
In this paper, we present a study aimed at understanding whether the embodiment and humanlikeness of an artificial agent can affect people's spontaneous and instructed mimicry of i…
Towards Teachable Autotelic Agents
Olivier Sigaud, Ahmed Akakzia, Hugo Caselles-Dupré +3
Autonomous discovery and direct instruction are two distinct sources of learning in children but education sciences demonstrate that mixed approaches such as assisted discovery or…
AudVowelConsNet: A Phoneme-Level Based Deep CNN Architecture for Clinical Depression Diagnosis
Muhammad Muzammel, Hanan Salam, Yann Hoffmann +2
Depression is a common and serious mood disorder that negatively affects the patient's capacity of functioning normally in daily tasks. Speech is proven to be a vigorous tool in de…
Language-Conditioned Goal Generation: a New Approach to Language Grounding for RL
Cédric Colas, Ahmed Akakzia, Pierre-Yves Oudeyer +2
In the real world, linguistic agents are also embodied agents: they perceive and act in the physical world. The notion of Language Grounding questions the interactions between lang…
Grounding Language to Autonomously-Acquired Skills via Goal Generation
Ahmed Akakzia, Cédric Colas, Pierre-Yves Oudeyer +2
We are interested in the autonomous acquisition of repertoires of skills. Language-conditioned reinforcement learning (LC-RL) approaches are great tools in this quest, as they allo…
Reinforcement learning with human advice: a survey
Anis Najar, Mohamed Chetouani
In this paper, we provide an overview of the existing methods for integrating human advice into a Reinforcement Learning process. We first propose a taxonomy of the different forms…
MobiAxis: An Embodied Learning Task for Teaching Multiplication with a Social Robot
Karen Tatarian, Sebastian Wallkotter, Sera Buyukgoz +2
The use of robots in educational settings is growing increasingly popular. Yet, many of the learning tasks involving social robots do not take full advantage of their physical embo…
Explainable Agents Through Social Cues: A Review
Sebastian Wallkotter, Silvia Tulli, Ginevra Castellano +2
The issue of how to make embodied agents explainable has experienced a surge of interest over the last three years, and, there are many terms that refer to this concept, e.g., tran…
Interactively shaping robot behaviour with unlabeled human instructions
Anis Najar, Olivier Sigaud, Mohamed Chetouani
In this paper, we propose a framework that enables a human teacher to shape a robot behaviour by interactively providing it with unlabeled instructions. We ground the meaning of in…
CLIC: Curriculum Learning and Imitation for object Control in non-rewarding environments
Pierre Fournier, Olivier Sigaud, Cédric Colas +1
In this paper we study a new reinforcement learning setting where the environment is non-rewarding, contains several possibly related objects of various controllability, and where…
CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning
Cédric Colas, Pierre Fournier, Olivier Sigaud +2
In open-ended environments, autonomous learning agents must set their own goals and build their own curriculum through an intrinsically motivated exploration. They may consider a l…
EEG-based Inter-Subject Correlation Schemes in a Stimuli-Shared Framework: Interplay with Valence and Arousal
Ayoub Hajlaoui, Mohamed Chetouani, Slim Essid
Affective computing is confronted to high inter-subject variability, in both emotional and physiological responses to a given stimulus. In a stimuli-shared framework, that is to sa…
Accuracy-based Curriculum Learning in Deep Reinforcement Learning
Pierre Fournier, Olivier Sigaud, Mohamed Chetouani +1
In this paper, we investigate a new form of automated curriculum learning based on adaptive selection of accuracy requirements, called accuracy-based curriculum learning. Using a r…
Trust as indicator of robot functional and social acceptance. An experimental study on user conformation to the iCub's answers
Ilaria Gaudiello, Elisabetta Zibetti, Sebastien Lefort +2
To investigate the functional and social acceptance of a humanoid robot, we carried out an experimental study with 56 adult participants and the iCub robot. Trust in the robot has…