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International audience; The interaction of robotics with behavioral and cog- nitive sciences has always been tight. As often described in the lit- erature, the living has inspired the construction of many robots. Yet, in this article, we focus on the reverse phenomenon: building robots can impact importantly the way we conceptualize behavior and cognition in animals and humans. This article presents a se- ries of paradigmatic examples spanning from the modelling of in- sect navigation, the experimentation of the role of morphology to control locomotion, the development of foundational representa- tions of the body and of the self/other distinction, the self-organi- zation of language in robot societies, and the use of robots as ther- apeutic tools for children with developmental disorders. Through these examples, I review the way robots can be used as operational models confronting specific theories to reality, or can be used as proof of concepts, or as conceptual exploration tools generating new hypotheses, or used as experimental set ups to uncover par- ticular behavioral properties in animals or humans, or even used as therapeutic tools. Finally, I discuss the fact that in spite of its role in the formation of many fundamental theories in behavioral and cognitive sciences, the use of robots is far from being accepted as a standard tool and contributions are often forgotten, leading to regular rediscoveries and slowing down cumulative progress. The article concludes by highlighting the high priority of further his- torical and epistemological work.

International audience; IAC was initially introduced as a developmental mechanism allowing a robot to self-organize developmental trajectories of increasing complexity without pre-programming the particular developmental stages. In this paper, we argue that IAC and other intrinsically motivated learning heuristics could be viewed as active learning algorithms that are particularly suited for learning forward models in unprepared sensorimotor spaces with large unlearnable subspaces. Then, we introduce a novel formulation of IAC, called R-IAC (Robust Intelligent Adaptive Curiosity), and show that its performances as an intrinsically motivated active learning algorithm are far superior to IAC in a complex sensorimotor space where only a small subspace is neither unlearnable nor trivial. We also show results in which the learnt forward model is reused in a control scheme. Finally, an open-source accompanying software containing these algorithms as well as tools to reproduce all the experiments presented in this paper is made publicly available. Index Terms-- active learning, intrinsic motivation, exploration, developmental robotics, artificial curiosity, sensorimotor learning.

International audience; We identify a strong structural similarity between the Gavagai problem in language acquisition and the problem of imitation learning of multiple context-dependent sensorimotor skills from human teachers. In both cases, a learner has to resolve concurrently multiple types of ambiguities while learning how to act in response to particular contexts through the observation of a teacher's demonstrations. We argue that computational models of language acquisition and models of motor skill learning by demonstration have so far only considered distinct subsets of these types of ambiguities, leading to the use of distinct families of techniques across two loosely connected research domains. We present a computational model, mixing concepts and techniques from these two domains, involving a simulated robot learner interacting with a human teacher. Proof-of-concept experiments show that: 1) it is possible to consider simultaneously a larger set of ambiguities than considered so far in either domain; 2) this allows us to model important aspects of language acquisition and motor learning within a single process that does not initially separate what is "linguistic" from what is "non-linguistic". Rather, the model shows that a general form of imitation learning can allow a learner to discover channels of communication used by an ambiguous teacher, thus addressing a form of abstract Gavagai problem (ambiguity about which observed behavior is "linguistic", and in that case which modality is communicative). Keywords: language acquisition, sensorimotor learning, imitation learning, motor Gavagai problem, discovering linguistic channels, robot learning by demonstration.