Interoceptive robustness through environment-mediated morphological development

Typically, AI researchers and roboticists try to realize intelligent behavior in machines by tuning parameters of a predefined structure (body plan and/or neural network architecture) using evolutionary or learning algorithms. Another but not unrelated longstanding property of these systems is their brittleness to slight aberrations, as highlighted by the growing deep learning literature on adversarial examples. Here we show robustness can be achieved by evolving the geometry of soft robots, their control systems, and how their material properties develop in response to one particular interoceptive stimulus (engineering stress) during their lifetimes. By doing so we realized robots that were equally fit but more robust to extreme material defects (such as might occur during fabrication or by damage thereafter) than robots that did not develop during their lifetimes, or developed in response to a different interoceptive stimulus (pressure). This suggests that the interplay between changes in the containing systems of agents (body plan and/or neural architecture) at different temporal scales (evolutionary and developmental) along different modalities (geometry, material properties, synaptic weights) and in response to different signals (interoceptive and external perception) all dictate those agents' abilities to evolve or learn capable and robust strategies

Regulating Highly Automated Robot Ecologies: Insights from Three User Studies

Highly automated robot ecologies (HARE), or societies of independent autonomous robots or agents, are rapidly becoming an important part of much of the world's critical infrastructure. As with human societies, regulation, wherein a governing body designs rules and processes for the society, plays an important role in ensuring that HARE meet societal objectives. However, to date, a careful study of interactions between a regulator and HARE is lacking. In this paper, we report on three user studies which give insights into how to design systems that allow people, acting as the regulatory authority, to effectively interact with HARE. As in the study of political systems in which governments regulate human societies, our studies analyze how interactions between HARE and regulators are impacted by regulatory power and individual (robot or agent) autonomy. Our results show that regulator power, decision support, and adaptive autonomy can each diminish the social welfare of HARE, and hint at how these seemingly desirable mechanisms can be designed so that they become part of successful HARE.Comment: 10 pages, 7 figures, to appear in the 5th International Conference on Human Agent Interaction (HAI-2017), Bielefeld, German

Neural development and sensorimotor control

What is the relationship between development of the nervous system and the emergence of voluntary motor behavior? This is the central question of the nature-nurture discussion that has intrigued child psychologists and pediatric neurologists for decades. This paper attempts to revisit this issue. Recent empirical evidence on how infants acquire multi-joint coordination and how children learn to adapt to novel force environments will be discussed with reference to the underlying development of the nervous system. The claim will be made that the developing human nervous system by no means constitutes an ideal controller. However, its redundancy, its ability to integrate multi-modal sensory information and motor commands and its facility of time-critical neural plasticity are features that may prove to be useful for the design of adaptive robots

Towards socially adaptive robots : A novel method for real time recognition of human-robot interaction styles

“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.” DOI: 10.1109/ICHR.2008.4756004Automatically detecting different styles of play in human-robot interaction is a key challenge towards adaptive robots, i.e. robots that are able to regulate the interactions and adapt to different interaction styles of the robot users. In this paper we present a novel algorithm for pattern recognition in human-robot interaction, the Cascaded Information Bottleneck Method. We apply it to real-time autonomous recognition of human-robot interaction styles. This method uses an information theoretic approach and enables to progressively extract relevant information from time series. It relies on a cascade of bottlenecks, the bottlenecks being trained one after the other according to the existing Agglomerative Information Bottleneck Algorithm. We show that a structure for the bottleneck states along the cascade emerges and we introduce a measure to extrapolate unseen data. We apply this method to real-time recognition of Human-Robot Interaction Styles by a robot in a detailed case study. The algorithm has been implemented for real interactions between humans and a real robot. We demonstrate that the algorithm, which is designed to operate real time, is capable of classifying interaction styles, with a good accuracy and a very acceptable delay. Our future work will evaluate this method in scenarios on robot-assisted therapy for children with autism.Peer reviewe

Multimodal Man-machine Interface and Virtual Reality for Assistive Medical Systems

The results of research the intelligence multimodal man-machine interface and virtual reality means for assistive medical systems including computers and mechatronic systems (robots) are discussed. The gesture translation for disability peoples, the learning-by-showing technology and virtual operating room with 3D visualization are presented in this report and were announced at International exhibition "Intelligent and Adaptive Robots–2005"

Evolutionary Modular Robotics: Survey and Analysis

This paper surveys various applications of artificial evolution in the field of modular robots. Evolutionary robotics aims to design autonomous adaptive robots automatically that can evolve to accomplish a specific task while adapting to environmental changes. A number of studies have demonstrated the feasibility of evolutionary algorithms for generating robotic control and morphology. However, a huge challenge faced was how to manufacture these robots. Therefore, modular robots were employed to simplify robotic evolution and their implementation in real hardware. Consequently, more research work has emerged on using evolutionary computation to design modular robots rather than using traditional hand design approaches in order to avoid cognition bias. These techniques have the potential of developing adaptive robots that can achieve tasks not fully understood by human designers. Furthermore, evolutionary algorithms were studied to generate global modular robotic behaviors including; self-assembly, self-reconfiguration, self-repair, and self-reproduction. These characteristics allow modular robots to explore unstructured and hazardous environments. In order to accomplish the aforementioned evolutionary modular robotic promises, this paper reviews current research on evolutionary robotics and modular robots. The motivation behind this work is to identify the most promising methods that can lead to developing autonomous adaptive robotic systems that require the minimum task related knowledge on the designer side.https://doi.org/10.1007/s10846-018-0902-