Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework

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 methods (based in particular on Deep Learning and Recurrent Neural Networks) with more traditional ones (e.g. Monte-Carlo tree search, goal babbling exploration or addressable memory systems). Regarding embodiment, we note that the traditional benchmark tasks (e.g. visual classification or board games) are becoming obsolete as state-of-the-art learning algorithms approach or even surpass human performance in most of them, having recently encouraged the development of first-person 3D game platforms embedding realistic physics. Building upon this analysis, we first propose an embodied cognitive architecture integrating heterogenous sub-fields of Artificial Intelligence into a unified framework. We demonstrate the utility of our approach by showing how major contributions of the field can be expressed within the proposed framework. We then claim that benchmarking environments need to reproduce ecologically-valid conditions for bootstrapping the acquisition of increasingly complex cognitive skills through the concept of a cognitive arms race between embodied agents.Comment: Updated version of the paper accepted to the ICDL-Epirob 2017 conference (Lisbon, Portugal

Why study movement variability in autism?

Autism has been defined as a disorder of social cognition, interaction and communication where ritualistic, repetitive behaviors are commonly observed. But how should we understand the behavioral and cognitive differences that have been the main focus of so much autism research? Can high-level cognitive processes and behaviors be identified as the core issues people with autism face, or do these characteristics perhaps often rather reflect individual attempts to cope with underlying physiological issues? Much research presented in this volume will point to the latter possibility, i.e. that people on the autism spectrum cope with issues at much lower physiological levels pertaining not only to Central Nervous Systems (CNS) function, but also to peripheral and autonomic systems (PNS, ANS) (Torres, Brincker, et al. 2013). The question that we pursue in this chapter is what might be fruitful ways of gaining objective measures of the large-scale systemic and heterogeneous effects of early atypical neurodevelopment; how to track their evolution over time and how to identify critical changes along the continuum of human development and aging. We suggest that the study of movement variability—very broadly conceived as including all minute fluctuations in bodily rhythms and their rates of change over time (coined micro-movements (Figure 1A-B) (Torres, Brincker, et al. 2013))—offers a uniquely valuable and entirely objectively quantifiable lens to better assess, understand and track not only autism but cognitive development and degeneration in general. This chapter presents the rationale firstly behind this focus on micro-movements and secondly behind the choice of specific kinds of data collection and statistical metrics as tools of analysis (Figure 1C). In brief the proposal is that the micro-movements (defined in Part I – Chapter 1), obtained using various time scales applied to different physiological data-types (Figure 1), contain information about layered influences and temporal adaptations, transformations and integrations across anatomically semi-independent subsystems that crosstalk and interact. Further, the notion of sensorimotor re-afference is used to highlight the fact that these layered micro-motions are sensed and that this sensory feedback plays a crucial role in the generation and control of movements in the first place. In other words, the measurements of various motoric and rhythmic variations provide an access point not only to the “motor systems”, but also access to much broader central and peripheral sensorimotor and regulatory systems. Lastly, we posit that this new lens can also be used to capture influences from systems of multiple entry points or collaborative control and regulation, such as those that emerge during dyadic social interactions

Empathy, engagement, entrainment: the interaction dynamics of aesthetic experience

A recent version of the view that aesthetic experience is based in empathy as inner imitation explains aesthetic experience as the automatic simulation of actions, emotions, and bodily sensations depicted in an artwork by motor neurons in the brain. Criticizing the simulation theory for committing to an erroneous concept of empathy and failing to distinguish regular from aesthetic experiences of art, I advance an alternative, dynamic approach and claim that aesthetic experience is enacted and skillful, based in the recognition of others’ experiences as distinct from one’s own. In combining insights from mainly psychology, phenomenology, and cognitive science, the dynamic approach aims to explain the emergence of aesthetic experience in terms of the reciprocal interaction between viewer and artwork. I argue that aesthetic experience emerges by participatory sense-making and revolves around movement as a means for creating meaning. While entrainment merely plays a preparatory part in this, aesthetic engagement constitutes the phenomenological side of coupling to an artwork and provides the context for exploration, and eventually for moving, seeing, and feeling with art. I submit that aesthetic experience emerges from bodily and emotional engagement with works of art via the complementary processes of the perception–action and motion–emotion loops. The former involves the embodied visual exploration of an artwork in physical space, and progressively structures and organizes visual experience by way of perceptual feedback from body movements made in response to the artwork. The latter concerns the movement qualities and shapes of implicit and explicit bodily responses to an artwork that cue emotion and thereby modulate over-all affect and attitude. The two processes cause the viewer to bodily and emotionally move with and be moved by individual works of art, and consequently to recognize another psychological orientation than her own, which explains how art can cause feelings of insight or awe and disclose aspects of life that are unfamiliar or novel to the viewer

Physically Embedded Genetic Algorithm Learning in Multi-Robot Scenarios: The PEGA algorithm

We present experiments in which a group of autonomous mobile robots learn to perform fundamental sensor-motor tasks through a collaborative learning process. Behavioural strategies, i.e. motor responses to sensory stimuli, are encoded by means of genetic strings stored on the individual robots, and adapted through a genetic algorithm (Mitchell, 1998) executed by the entire robot collective: robots communicate their own strings and corresponding fitness to each other, and then execute a genetic algorithm to improve their individual behavioural strategy. The robots acquired three different sensormotor competences, as well as the ability to select one of two, or one of three behaviours depending on context ("behaviour management"). Results show that fitness indeed increases with increasing learning time, and the analysis of the acquired behavioural strategies demonstrates that they are effective in accomplishing the desired task

Beyond Gazing, Pointing, and Reaching: A Survey of Developmental Robotics

Developmental robotics is an emerging field located at the intersection of developmental psychology and robotics, that has lately attracted quite some attention. This paper gives a survey of a variety of research projects dealing with or inspired by developmental issues, and outlines possible future directions

Grounding action in visuo-haptic space using experience networks

Traditional approaches to the use of machine learning algorithms do not provide a method to learn multiple tasks in one-shot on an embodied robot. It is proposed that grounding actions within the sensory space leads to the development of action-state relationships which can be re-used despite a change in task. A novel approach called an Experience Network is developed and assessed on a real-world robot required to perform three separate tasks. After grounded representations were developed in the initial task, only minimal further learning was required to perform the second and third task