Understanding the Uncertainty Loop of Human-Robot Interaction

Recently the field of Human-Robot Interaction gained popularity, due to the wide range of possibilities of how robots can support humans during daily tasks. One form of supportive robots are socially assistive robots which are specifically built for communicating with humans, e.g., as service robots or personal companions. As they understand humans through artificial intelligence, these robots will at some point make wrong assumptions about the humans' current state and give an unexpected response. In human-human conversations, unexpected responses happen frequently. However, it is currently unclear how such robots should act if they understand that the human did not expect their response, or even showing the uncertainty of their response in the first place. For this, we explore the different forms of potential uncertainties during human-robot conversations and how humanoids can, through verbal and non-verbal cues, communicate these uncertainties

Reservoir SMILES: Towards SensoriMotor Interaction of Language and Embodiment of Symbols with Reservoir Architectures

Language involves several hierarchical levels of abstraction. Most models focus on a particular level of abstraction making them unable to model bottom-up and top-down processes. Moreover, we do not know how the brain grounds symbols to perceptions and how these symbols emerge throughout development. Experimental evidence suggests that perception and action shape one-another (e.g. motor areas activated during speech perception) but the precise mechanisms involved in this action-perception shaping at various levels of abstraction are still largely unknown. My previous and current work include the modelling of language comprehension, language acquisition with a robotic perspective, sensorimotor models and extended models of Reservoir Computing to model working memory and hierarchical processing. I propose to create a new generation of neural-based computational models of language processing and production; to use biologically plausible learning mechanisms relying on recurrent neural networks; create novel sensorimotor mechanisms to account for action-perception shaping; build hierarchical models from sensorimotor to sentence level; embody such models in robots

The Ecology of Open-Ended Skill Acquisition: Computational framework and experiments on the interactions between environmental, adaptive, multi-agent and cultural dynamics

An intriguing feature of the human species is our ability to continuously invent new problems and to proactively acquiring new skills in order to solve them: what is called open-ended skill acquisition (OESA). Understanding the mechanisms underlying OESA is an important scientific challenge in both cognitive science (e.g. by studying infant cognitive development) and in artificial intelligence (aiming at computational architectures capable of open-ended learning). Both fields, however, mostly focus on cognitive and social mechanisms at the scale of an individual’s life. It is rarely acknowledged that OESA, an ability that is fundamentally related to the characteristics of human intelligence, has been necessarily shaped by ecological, evolutionary and cultural mechanisms interacting at multiple spatiotemporal scales. In this thesis, I present a research program aiming at understanding, modelingand simulating the dynamics of OESA in artificial systems, grounded in theories studying its eco-evolutionary bases in the human species. It relies on a conceptual framework expressing the complex interactions between environmental, adaptive, multi-agent and cultural dynamics. Three main research questions are developed and I present a selection of my contributions for each of them.- What are the ecological conditions favoring the evolution of skill acquisition?- How to bootstrap the formation of a cultural repertoire in populations of adaptive agents?- What is the role of cultural evolution in the open-ended dynamics of human skill acquisition?By developing these topics, we will reveal interesting relationships between theories in human evolution and recent approaches in artificial intelligence. This will lead to the proposition of a humanist perspective on AI: using it as a family of computational tools that can help us to explore and study the mechanisms driving open-ended skill acquisition in both artificial and biological systems, as a way to better understand the dynamics of our own species within its whole ecological context. This document presents an overview of my scientific trajectory since the start of my PhD thesis in 2007, the detail of my current research program, a selection of my contributions as well as perspectives for future work

Tool-Use Model to Reproduce the Goal Situations Considering Relationship Among Tools, Objects, Actions and Effects Using Multimodal Deep Neural Networks

We propose a tool-use model that enables a robot to act toward a provided goal. It is important to consider features of the four factors; tools, objects actions, and effects at the same time because they are related to each other and one factor can influence the others. The tool-use model is constructed with deep neural networks (DNNs) using multimodal sensorimotor data; image, force, and joint angle information. To allow the robot to learn tool-use, we collect training data by controlling the robot to perform various object operations using several tools with multiple actions that leads different effects. Then the tool-use model is thereby trained and learns sensorimotor coordination and acquires relationships among tools, objects, actions and effects in its latent space. We can give the robot a task goal by providing an image showing the target placement and orientation of the object. Using the goal image with the tool-use model, the robot detects the features of tools and objects, and determines how to act to reproduce the target effects automatically. Then the robot generates actions adjusting to the real time situations even though the tools and objects are unknown and more complicated than trained ones

Unsupervised Phoneme and Word Discovery from Multiple Speakers using Double Articulation Analyzer and Neural Network with Parametric Bias

This paper describes a new unsupervised machine learning method for simultaneous phoneme and word discovery from multiple speakers. Human infants can acquire knowledge of phonemes and words from interactions with his/her mother as well as with others surrounding him/her. From a computational perspective, phoneme and word discovery from multiple speakers is a more challenging problem than that from one speaker because the speech signals from different speakers exhibit different acoustic features. This paper proposes an unsupervised phoneme and word discovery method that simultaneously uses nonparametric Bayesian double articulation analyzer (NPB-DAA) and deep sparse autoencoder with parametric bias in hidden layer (DSAE-PBHL). We assume that an infant can recognize and distinguish speakers based on certain other features, e.g., visual face recognition. DSAE-PBHL is aimed to be able to subtract speaker-dependent acoustic features and extract speaker-independent features. An experiment demonstrated that DSAE-PBHL can subtract distributed representations of acoustic signals, enabling extraction based on the types of phonemes rather than on the speakers. Another experiment demonstrated that a combination of NPB-DAA and DSAE-PB outperformed the available methods in phoneme and word discovery tasks involving speech signals with Japanese vowel sequences from multiple speakers.Comment: 21 pages. Submitte

Cognitive neurorobotics and self in the shared world, a focused review of ongoing research

Through brain-inspired modeling studies, cognitive neurorobotics aims to resolve dynamics essential to different emergent phenomena at the level of embodied agency in an object environment shared with human beings. This article is a review of ongoing research focusing on model dynamics associated with human self-consciousness. It introduces the free energy principle and active inference in terms of Bayesian theory and predictive coding, and then discusses how directed inquiry employing analogous models may bring us closer to representing the sense of self in cognitive neurorobots. The first section quickly locates cognitive neurorobotics in the broad field of computational cognitive modeling. The second section introduces principles according to which cognition may be formalized, and reviews cognitive neurorobotics experiments employing such formalizations. The third section interprets the results of these and other experiments in the context of different senses of self, both “minimal” and “narrative” self. The fourth section considers model validity and discusses what we may expect ongoing cognitive neurorobotics studies to contribute to scientific explanation of cognitive phenomena including the senses of minimal and narrative self