Automating Automation: Lessons from R.U.R about the Future of Evolutionary Robotics

R.U.R: Rossum’s Universal Robots presents a narrative that discusses the age-old postulate for what separates humankind and automatons. That is, the dichotomy between us and them is that our mechanical servants (robots) are made not born. Self-replication has been a long standing open research problem and topic of discussion in artificial life, with a range of highly anticipated future macro-robotic to nano-robotic applications. More recently, self-replication has been the subject of some research attention in the relatively embryonic field of evolutionary robotics and the topic has even enjoyed some international media attention. One observation drawn from evolutionary robotics research as a whole, is that much like the robots of R.U.R, current experimental evolutionary robotic systems are inexorably tied to their system designers. Dissimilar to the biological counter-parts that they aspire to, such robotic systems are not self-sufficient. Even though such robots have some autonomy in specific environments, they are unable to autonomously propagate and improve their body-brain design on an evolutionary time-scale

Environmental Impact on Evolving Language Diversity

There has been a significant amount of research on computational modeling of language evolution to understand the origins and evolution of communication. However, there has been relatively little computational modeling of environmental factors influencing the evolution of linguistic diversity and thus the emergence and merging of dialects. Using evolutionary agent-based simulation, this study investigates environmental factors influencing the emergence of linguistic diversity in an evolving agentbased language simulation. We used iterative agent-based naming game simulations to evaluate the impact resources, population, and environment size have on evolving language diversity. A specific aim was to investigate thresholds (tipping-points) in factors that cause significant changes to linguistic diversity in populations

The Role of Speaker Prestige in Synthetic Language Evolution

Many researchers hypothesize that language adaptation, as with other evolutionary processes, entails both directed selection and random drift. However, the specific contributions of these processes to language evolution remains an open question. It is well established that language evolution is not necessarily driven by selection, for example, speakers preferring specific word variants. Extending related work, we use computational agent-based models to elucidate the impact of individual-level bias (speaker prestige) on population-level dynamics (average word similarity), where word diversity is measured by Levenshtein similarity. Agents interacted in iterative language games, to name and thus converse about resource types (A, B). Such object types represented conversation topics, where resource value indicated agent bias for conversing about (evolving words for) popular topics. For a null model comparison, we comparatively evaluated random drift versus directed word evolution on evolving word similarity, where using directed evolution, agent bias for adopting specific words (about resource types) increased with speaker agent social prestige (fitness). While previous work has demonstrated selective advantages of various forms of speaker sociolinguistic prestige including competing word variants and borrowed words, there has been little research on the impact of speaker prestige on word diversity in language evolution

Evolving an Artificial Creole

There has been a significant amount of research on computational modeling of language evolution to understand the origins and evolution of communication. However, there has relatively been relatively little computational modeling of environmental factors that enable the evolution of creole languages, specifically, modeling lexical term transmission between intersecting language groups, within the context of artificial creole language evolution. This study used an iterative agent-based naming game simulation to investigate the impact of population size and lexical similarity of interacting language groups on the evolution of an artificial creole lexicon. We applied the synthetic methodology, using agent-based artificial language evolution as an experimental platform to investigate two objectives. First, to investigate the impact of population size of interacting groups (with differing lexicons) on the evolution of a common (creole) lexicon. Second, to evaluate the concurrent impact of lexical similarity between interacting agent groups on the evolution of a creole lexicon

Acolhimento temporário institucional

RESUMO Este trabalho tem como objectivo principal um estudo de campo no Centro de Acolhimento Temporário Gracinda Tito com uma população de 18 crianças e jovens institucionalizados da RAM. Pretende apresentar a temática teórica e legal do acolhimento temporário, caracterizar a instituição-alvo e os participantes no estudo e, finalmente, tentar saber a razão pelo qual as crianças/jovens foram acolhidas, bem como a medida de promoção e protecção aplicada a cada criança e jovem e o seu projecto de vida futuro.ABSTRACT The main objective of this research work about temporary institutional reception, aims to study the eighteen children and young people received at Gracinda Tito Temporary Reception Center. This research comes to know the reason why children and young people were received, as well as the promotion and protection measure applied to every child and young, and their future life project

Energy and Complexity in Evolving Collective Robot Bodies and Brains

The impact of the environment on evolving increasingly complex morphologies (bodies) and controllers (brains) remains an open question in evolutionary biology and has important implications for the evolutionary design of robots. This study uses evolutionary robotics as an experimental platform to evaluate relationships between environment complexity and evolving bodybrain complexity given energy costs on evolving complexity. We evolve robot body-brain designs for increasingly complex environments (difficult cooperative transport tasks) in a collective robotic gathering simulation. The impact of complexity costs on body-brain evolution is evaluated across such increasingly complex environments. Results indicate that complexity costs enable the evolution of simpler body-brain designs that are effective in simple environments but yield negligible behavior (task performance) differences in more complex environments

Evolutionary Automation of Coordinated Autonomous Vehicles

Recently, there has been increased research on adaptive control systems for vehicles that operate on autonomous vehicle only roads. Specifically, roads without current infrastructure constraints of traffic lights, stop signals at intersections or vehicle lanes. This study investigates controller automation for vehicles that must navigate and coordinate with each other on such autonomous vehicle only roads. We comparatively evaluate fitness-function (objective) versus behavior-based (novelty search) versus hybridized objective-novelty evolutionary search for synthesizing autonomous vehicle coordinated driving behavior. The goal of such evolved coordinated driving behavior is to maximize effective (safe) and efficient (expedient) autonomous vehicle traffic throughput for given roads. Results indicate that while novelty and hybrid search evolved effective and efficient driving behaviors, these behaviors did not generalize to new roads as well as driving behaviors evolved with objective-based search

The Impact of Morphological Diversity in Robot Swarms

In nature, morphological diversity enhances functional diversity, however, there is little swarm (collective) robotics research on the impact of morphological and behavioral (body-brain) diversity that emerges in response to changing environments. This study investigates the impact of increasingly complex task environments on the artificial evolution of body-brain diversity in simulated robot swarms. We investigate whether increasing task environment complexity (collective behavior tasks requiring increasing degrees of cooperative behavior) mandates concurrent increases in behavioral, morphological, or coupled increases in body-brain diversity in robotic swarms. Experiments compared three variants of collective behavior evolution across increasingly complex task environments: two behavioral diversity maintenance variants and body-brain diversity maintenance. Results indicate that body-brain diversity maintenance yielded a significantly higher behavioral and morphological diversity in evolved swarms overall, which was beneficial in the most complex task environment

Evolving Gaits for Damage Control in a Hexapod Robot

Autonomous robots are increasingly used in remote and hazardous environments, where damage to sensory-actuator systems cannot be easily repaired. Such robots must therefore have controllers that continue to function effectively given unexpected malfunctions and damage to robot morphology. This study applies the Intelligent Trial and Error (IT&E) algorithm to adapt hexapod robot control to various leg failures and demonstrates the IT&E map-size parameter as a critical parameter in influencing IT&E adaptive task performance. We evaluate robot adaptation for multiple leg failures on two different map-sizes in simulation and validate evolved controllers on a physical hexapod robot. Results demonstrate a trade-off between adapted gait speed and adaptation duration, dependent on adaptation task complexity (leg damage incurred), where map-size is crucial for generating behavioural diversity required for adaptation

AutoFac: The Perpetual Robot Machine

Robotics currently lacks fully autonomous capabilities, especially where task knowledge is incomplete and optimal robotic solutions cannot be pre-engineered. The intersection of evolutionary robotics, artificial life and embodied artificial intelligence presents a promising paradigm for generating multitask problem-solvers suitable for adapting over extended periods in unexplored, remote and hazardous environments. To address the automation of evolving robotic systems, we propose fully autonomous, embodied artificial-life factories and laboratories, situated in various environments as multi-task problem-solvers. Such integrated factories and laboratories would be adaptive solution designers, producing fit-for-purpose physical robots with accelerated artificial evolution that experiment to continually discover new tasks. Such tasks would be stepping-stones towards accomplishing given mission objectives over extended periods (days to decades). Rather than being purely speculative, prerequisite technologies to realize such factories have been experimentally demonstrated. Currently, vast scientific and enterprise opportunities await in applications such as asteroid mining, terraforming, space and deep sea exploration, though no suitable solution exists. The proposed embodied artificial-life factories and laboratories, termed: AutoFac, use robot production equipment run by artificial evolution controllers to collect and synthesize environmental information (from robotic sensory systems). Such information is merged with current needs and mission objectives to create new robot embodiment and task definitions that are environmentally adapted and balance task-oriented behavior with exploration. AutoFac is thus generalist (deployable in many environments) but continually produces specialist solutions within such environments — a perpetual robot machine