Clément Moulin-Frier

University Pompeu Fabra | UPF · Laboratory for Synthetic Perceptive, Emotive and Cognitive Systems (SPECS)

Developing methods to explore, predict and control the dynamic behavior of biological systems, from protein pathways to complex cellular processes, is an essential frontier of research for bioengineering and biomedicine. Thus, significant effort has gone in computational inference and mathematical modeling of biological systems. This effort has res...

In both natural and artificial studies, evolution is often seen as synonymous to natural selection. Individuals evolve under pressures set by environments that are either reset or do not carry over significant changes from previous generations. Thus, niche construction (NC), the reciprocal process to natural selection where individuals incur inheri...

In this perspective article, we show that a morphospace, based on information-theoretic measures, can be a useful construct for comparing biological agents with artificial intelligence (AI) systems. The axes of this space label three kinds of complexity: (i) autonomic, (ii) computational and (iii) social complexity. On this space, we map biological...

Neuroevolution (NE) has recently proven a competitive alternative to learning by gradient descent in reinforcement learning tasks. However, the majority of NE methods and associated simulation environments differ crucially from biological evolution: the environment is reset to initial conditions at the end of each generation, whereas natural enviro...

Lenia is a family of cellular automata (CA) generalizing Conway's Game of Life to continuous space, time and states. Lenia has attracted a lot of attention because of the wide diversity of self-organizing patterns it can generate. Among those, some spatially localized patterns (SLPs) resemble life-like artificial creatures. However, those creatures...

Humans have been able to tackle biosphere complexities by acting as ecosystem engineers, profoundly changing the flows of matter, energy and information. This includes major innovations that allowed to reduce and control the impact of extreme events. Modelling the evolution of such adaptive dynamics can be challenging given the potentially large nu...

Building autonomous agents able to grow open-ended repertoires of skills across their lives is a fundamental goal of artificial intelligence (AI). A promising developmental approach recommends the design of intrinsically motivated agents that learn new skills by generating and pursuing their own goals—autotelic agents. But despite recent progress,...

How can a population of reinforcement learning agents autonomously learn a diversity of cooperative tasks in a shared environment? In the single-agent paradigm, goal-conditioned policies have been combined with intrinsic motivation mechanisms to endow agents with the ability to master a wide diversity of autonomously discovered goals. Transferring...

The framework of Language Games studies the emergence of languages in populations of agents. Recent contributions relying on deep learning methods focused on agents communicating via an idealized communication channel, where utterances produced by a speaker are directly perceived by a listener. This comes in contrast with human communication, which...

The human cultural repertoire relies on innovation: our ability to continuously and hierarchically explore how existing elements can be combined to create new ones. Innovation is not solitary, it relies on collective accumulation and merging of previous solutions. Machine learning approaches commonly assume that fully connected multi-agent networks...

Building autonomous artificial agents able to grow open-ended repertoires of skills is one of the fundamental goals of AI. To that end, a promising developmental approach recommends the design of intrinsically motivated agents that learn new skills by generating and pursuing their own goals - autotelic agents. However, existing algorithms still sho...

Humans constantly search for and use information to solve a wide range of problems related to survival, social interactions, and learning. While it is clear that curiosity and the drive for knowledge occupies a central role in defining what being human means to ourselves, where does this desire to know the unknown come from? What is its purpose? An...

The diversity and quality of natural systems has been a puzzle and inspiration for communities studying artificial life. It is now widely admitted that the adaptation mechanisms enabling these properties are largely influenced by the environments they inhabit. Organisms facing environmental variability have two alternative adaptation mechanisms ope...

Language is an interface to the outside world. In order for embodied agents to use it, language must be grounded in other, sensorimotor modalities. While there is an extended literature studying how machines can learn grounded language, the topic of how to learn spatio-temporal linguistic concepts is still largely uncharted. To make progress in thi...

Intrinsically motivated information-seeking, also called curiosity-driven exploration, is widely believed to be a key ingredient for autonomous learning in the real world. Such forms of spontaneous exploration have been studied in multiple independent lines of computational research, producing a diverse range of algorithmic models that capture diff...

Effective latent representations need to capture abstract features of the externalworld. We hypothesise that the necessity for a group of agents to reconcile theirsubjective interpretations of a shared environment state is an essential factor in-fluencing this property. To test this hypothesis, we propose an architecture whereindividual agents in a...

In cognitive science, Theory of Mind (ToM) is the mental faculty of assessing intentions and beliefs of others and requires, in part, to distinguish incoming sensorimotor (SM) signals and, accordingly, attribute these to either the self-model, the model of the other, or one pertaining to the external world, including inanimate objects. To gain an u...

Modeling the dynamics of epidemics helps to propose control strategies based on pharmaceuticaland non-pharmaceutical interventions (contact limitation, lockdown, vaccination,etc). Hand-designing such strategies is not trivial because of the number of possibleinterventions and the difficulty to predict long-term effects. This task can be cast as an...

Language is an interface to the outside world. In order for embodied agents to use it, language must be grounded in other, sensorimotor modalities. While there is an extended literature studying how machines can learn grounded language, the topic of how to learn spatio-temporal linguistic concepts is still largely uncharted. To make progress in thi...

Recent advances in Artificial Intelligence (AI) have revived the quest for agents able to acquire an open-ended repertoire of skills. However, although this ability is fundamentally related to the characteristics of human intelligence, research in this field rarely considers the processes that may have guided the emergence of complex cognitive capa...

Developmental machine learning studies how artificial agents can model the way children learn open-ended repertoires of skills. Such agents need to create and represent goals, select which ones to pursue and learn to achieve them. Recent approaches have considered goal spaces that were either fixed and hand-defined or learned using generative model...

Epidemiologists model the dynamics of epidemics in order to propose control strategies based on pharmaceutical and non-pharmaceutical interventions (contact limitation, lock down, vaccination, etc). Hand-designing such strategies is not trivial because of the number of possible interventions and the difficulty to predict long-term effects. This tas...

Infant vocal babbling strongly relies on jaw oscillations, especially at the stage of canonical babbling, which underlies the syllabic structure of world languages. In this paper, we propose, model and analyze an hypothesis to explain this predominance of the jaw in early babbling. This hypothesis states that general stochastic optimization princip...

Self-organization of complex morphological patterns from local interactions is a fascinating phenomenon in many natural and artificial systems. In the artificial world, typical examples of such morphogenetic systems are cellular automata. Yet, their mechanisms are often very hard to grasp and so far scientific discoveries of novel patterns have pri...

What is the role of real-time control and learning in the formation of social conventions? To answer this question, we propose a computational model that matches human behavioral data in a social decision-making game that was analyzed both in discrete-time and continuous-time setups. Furthermore, unlike previous approaches, our model takes into acc...

This paper investigates the idea of encoding object-centered representations in the design of the reward function and policy architectures of a language-guided reinforcement learning agent. This is done using a combination of object-wise permutation invariant networks inspired from Deep Sets and gated-attention mechanisms. In a 2D procedurally-gene...

Autonomous reinforcement learning agents must be intrinsically motivated to explore their environment, discover potential goals, represent them and learn how to achieve them. As children do the same, they benefit from exposure to language, using it to formulate goals and imagine new ones as they learn their meaning. In our proposed learning archite...

Computational models of emergent communication in agent populations are currently gaining interest in the machine learning community due to recent advances in Multi-Agent Reinforcement Learning (MARL). Current contributions are however still relatively disconnected from the earlier theoretical and computational literature aiming at understanding ho...

The mechanisms of how the brain orchestrates multi-limb joint action have yet to be elucidated and few computational sensorimotor (SM) learning approaches have dealt with the problem of acquiring bimanual affordances. We propose a series of bidirectional (forward/inverse) SM maps and its associated learning processes that generalize from uni- to bi...

This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot. The framework, based on a biologically-grounded theory of the brain and mind, integrates a reactive interaction engine,...

Generating complex, human-like behavior in a humanoid robot like the iCub requires the integration of a wide range of open source components and a scalable cognitive architecture. Hence, we present the iCub-HRI library which provides convenience wrappers for components related to perception (object recognition, agent tracking, speech recognition, a...

In order to understand the formation of social conventions we need to know the specific role of control and learning in multi-agent systems. To advance in this direction, we propose, within the framework of the Distributed Adaptive Control (DAC) theory, a novel Control-based Reinforcement Learning architecture (CRL) that can account for the acquisi...

A first step to reach Theory of Mind (ToM) abilities (attribution of beliefs to others) in synthetic agents through sensorimotor interactions, would be to tag sensory data with agent typology and action intentions: autonomous agent X moved an object under the box. We propose a dual arm robotic setup in which ToM could be probed. We then discuss wha...

Given recent proposals to synthesize consciousness, how many forms of conscious machines can one distinguish and on what grounds? Based on current clinical scales of consciousness, that measure cognitive awareness and wakefulness, we take a perspective on how contemporary artificially intelligent machines and synthetically engineered life forms wou...

The aim of this article is to highlight the role of consciousness as a survival strategy in a complex multi-agent social environment. Clinical approaches to investigating consciousness usually center around cognitive awareness and arousal. An evolutionary approach to the problem offers a complimentary perspective demonstrating how social games trig...

Infant vocal babbling strongly relies on jaw oscillations, especially at the stage of canonical babbling, which underlies the syllabic structure of world languages. In this paper, we propose, model and analyze an hypothesis to explain this predominance of the jaw in early babbling. This hypothesis states that general stochastic optimization princip...

Given recent proposals to synthesize consciousness, how many forms of conscious machines can one distinguish and on what grounds? Based on current clinical scales of consciousness, that measure cognitive awareness and wakefulness, we take a perspective on how contemporary artificially intelligent machines and synthetically engineered life forms wou...

This work introduces new results on the modeling of early-vocal development using artificial intelligent cognitive architectures and a simulated vocal tract. The problem is addressed using intrinsically-motivated learning algorithms for autonomous sensorimotor exploration, a kind of algorithm belonging to the active learning architectures family. T...

In this paper, we argue that the future of Artificial Intelligence research resides in two keywords: integration and embodiment. We support this claim by analyzing the recent advances of the field. Regarding integration, we note that the most impactful recent contributions have been made possible through the integration of recent Machine Learning m...

Robots, as well as machine learning algorithms, have proven to be, unlike human beings, very sensitive to errors and failure. Artificial intelligence and machine learning are nowadays the main source of algorithms that drive cognitive robotics research. The advances in the fields have been huge during the last year, beating expert-human performance...

The contribution by M.A. Arbib over the years and as it appears summarized and conceptualized in this paper is admirable, extremely impressive, and very convincing in many aspects. A key value of this work is that it systematically attempts to introduce formal conceptualization and modeling in the reasoning about facts and interpretations.

This work introduces new results on early-vocal development in infants and machines using artificial intelligent agents. It is addressed using the perspective of intrinsically-motivated learning algorithms for autonomous exploration. The agent autonomously selects goals to explore its own sensorimotor system in regions where a certain competence me...

While the origin of language remains a somewhat mysterious process, understanding how human language takes specific forms appears to be accessible by the experimental method. Languages, despite their wide variety, display obvious regularities. In this paper, we attempt to derive some properties of phonological systems (the sound systems for human l...

The cerebellum has an important role on motor learning. How sensory data arrives to the cerebellum is hardly understood. A two-phase model is proposed to understand how raw sensory data is processed to facilitate cerebellar predictive learning. Different candidates are presented for guiding the perceptual learning phase grounded on the role of the...

The aim of this paper is to propose and computationally support an original hypothesis regarding the predominance of jaw movements in infant speech development. We capitalize on previous research on emergent maturations from a stochastic optimization process on the arm domain. This work has shown that a quite simple optimization process, allowing a...

We present an open-source Python library, called Explauto, providing a unified API to design and compare various exploration strategies driving various sensorimotor learning algorithms in various simulated or robotics systems. Explauto aims at being collaborative and pedagogic, providing a platform to developmental roboticists where they can publis...

We propose a new approach for solving a class of discrete decision making problems under uncertainty with positive cost. This issue concerns multiple and diverse fields such as engineering, economics, artificial intelligence, cognitive science and many others. Basically, an agent has to choose a single or series of actions from a set of options, wi...

Learning complex mappings between various modalities (typically articulatory, somato­sensory and auditory) is a central issue in computationally modeling speech acquisition. These mappings are generally non­linear and redundant, involving high dimensional sensorimotor spaces. Classical approaches consider two separate phases: a relatively pre-deter...

We present a probabilistic framework unifying two important families of exploration mechanisms recently shown to be efficient to learn complex non-linear redundant sensorimotor mappings. These two explorations mechanisms are: 1) goal babbling, 2) active learning driven by the maximization of empirically measured learning progress. We show how this...

We consider a computational model comparing the possible roles of "association" and "simulation" in phonetic decoding, demonstrating that these two routes can contain similar information in some "perfect" communication situations and highlighting situations where their decoding performance differs. We conclude that optimal decoding should involve s...

The motor theory of speech perception holds that we perceive the speech of another in terms of a motor representation of that speech. However, when we have learned to recognize a foreign accent, it seems plausible that recognition of a word rarely involves reconstruction of the speech gestures of the speaker rather than the listener. To better asse...

We bridge the gap between two issues in infant development: vocal development and intrinsic motivation. We propose and experimentally test the hypothesis that general mechanisms of intrinsically motivated spontaneous exploration, also called curiosity-driven learning, can self-organize developmental stages during early vocal learning. We introduce...

This article studies how developmental phonetic learning can be guided by pure curiosity-driven exploration, also called intrinsically motivated exploration. Phonetic learning refers here to learning how to control a vocal tract to reach acoustic goals. We compare three different exploration strategies for learning the auditory-motor inverse model:...

In this paper, we put forward a computational framework for the comparison between motor, auditory and perceptuo-motor theories of speech communication. We first recall the basic arguments of these three sets of theories, either applied to speech perception or to speech production. Then we expose a unifying Bayesian model able to express each theor...

If the origin of language is difficult to properly study, the origin of its forms appears to be accessible to the experimental method. Languages, despite their large variety, display obvious regularities, the linguistic universals. We study them through more general reasoning about language emergence, in particular in the search of its precursors,...