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

Analyzing children's expectations from robotic companions in educational settings

The use of robots as educational partners has been extensively explored, but less is known about the required characteristics these robots should have to meet children's expectations. Thus the purpose of this study is to analyze children's assumptions regarding morphology, functionality, and body features, among others, that robots should have to interact with them. To do so, we analyzed 142 drawings from 9 to 10 years old children and their answers to a survey provided after interacting with different robotic platforms. The main results convey on a gender-less robot with anthropomorphic (but machine-like) characteristics

The perceptual shaping of anticipatory actions

Humans display anticipatory motor responses to minimize the adverse effects of predictable perturbations. A widely accepted explanation for this behaviour relies on the notion of an inverse model that, learning from motor errors, anticipates corrective responses. Here, we propose and validate the alternative hypothesis that anticipatory control can be realized through a cascade of purely sensory predictions that drive the motor system, reflecting the causal sequence of the perceptual events preceding the error. We compare both hypotheses in a simulated anticipatory postural adjustment task. We observe that adaptation in the sensory domain, but not in the motor one, supports the robust and generalizable anticipatory control characteristic of biological systems. Our proposal unites the neurobiology of the cerebellum with the theory of active inference and provides a concrete implementation of its core tenets with great relevance both to our understanding of biological control systems and, possibly, to their emulation in complex artefacts

The role of a cerebellum-driven perceptual prediction within a robotic postural task

Abstract Postural adjustments are acquired compensatory and anticipatory motor responses maintaining balance and equilibrium against self-induced or external perturbations. It has been proposed that the cerebellum could be involved in issuing such predictive motor action

Allostatic control for robot behavior regulation: a comparative rodent-robot study

Rodents are optimal real-world foragers that regulate internal states maintaining a dynamic stability with their surroundings. How these internal drive based behaviors are regulated remains unclear. Based on the physiological notion of allostasis, we investigate a minimal control system able to approximate their behavior. Allostasis is the process of achieving stability with the environment through change, opposed to homeostasis which achieves it through constancy. Following this principle, the so-called allostatic control system orchestrates the interaction of the homeostatic modules by changing their desired values in order to achieve stability. We use a minimal number of subsystems and estimate the model parameters from rat behavioral data in three experimental setups: free exploration, presence of reward, delivery of cues with reward predictive value. From this analysis, we show that a rat is influenced by the shape of the arena in terms of its openness. We then use the estimated model configurations to control a simulated and real robot which captures essential properties of the observed rat behavior. The allostatic reactive control model is proposed as an augmentation of the Distributed Adaptive Control architecture and provides a further contribution towards the realization of an artificial rodent

Supplementary Figures from The perceptual shaping of anticipatory actions

Humans display anticipatory motor responses to minimize the adverse effects of predictable perturbations. A widely accepted explanation for this behaviour relies on the notion of an inverse model that, learning from motor errors, anticipates corrective responses. Here, we propose and validate the alternative hypothesis that anticipatory control can be realized through a cascade of purely sensory predictions that drive the motor system, reflecting the causal sequence of the perceptual events preceding the error. We compare both hypotheses in a simulated anticipatory postural adjustment task. We observe that adaptation in the sensory domain, but not in the motor one, supports the robust and generalizable anticipatory control characteristic of biological systems. Our proposal unites the neurobiology of the cerebellum with the theory of active inference and provides a concrete implementation of its core tenets with great relevance both to our understanding of biological control systems and, possibly, to their emulation in complex artefacts

ALLOSTATIC CONTROL FOR ROBOT BEHAVIOR REGULATION: A COMPARATIVE RODENT-ROBOT STUDY

Rodents are optimal real-world foragers that regulate internal states maintaining a dynamic stability with their surroundings. How these internal drive based behaviors are regulated remains unclear. Based on the physiological notion of allostasis, we investigate a minimal control system able to approximate their behavior. Allostasis is the process of achieving stability with the environment through change, opposed to homeostasis which achieves it through constancy. Following this principle, the so-called allostatic control system orchestrates the interaction of the homeostatic modules by changing their desired values in order to achieve stability. We use a minimal number of subsystems and estimate the model parameters from rat behavioral data in three experimental setups: free exploration, presence of reward, delivery of cues with reward predictive value. From this analysis, we show that a rat is influenced by the shape of the arena in terms of its openness. We then use the estimated model configurations to control a simulated and real robot which captures essential properties of the observed rat behavior. The allostatic reactive control model is proposed as an augmentation of the Distributed Adaptive Control architecture and provides a further contribution towards the realization of an artificial rodent.Homeostasis, allostasis, rodent behavior, behavioral-based robotics