Evolutionary robotics and neuroscience

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Incremental embodied chaotic exploration of self-organized motor behaviors with proprioceptor adaptation

This paper presents a general and fully dynamic embodied artificial neural system, which incrementally explores and learns motor behaviors through an integrated combination of chaotic search and reflex learning. The former uses adaptive bifurcation to exploit the intrinsic chaotic dynamics arising from neuro-body-environment interactions, while the latter is based around proprioceptor adaptation. The overall iterative search process formed from this combination is shown to have a close relationship to evolutionary methods. The architecture developed here allows realtime goal-directed exploration and learning of the possible motor patterns (e.g., for locomotion) of embodied systems of arbitrary morphology. Examples of its successful application to a simple biomechanical model, a simulated swimming robot, and a simulated quadruped robot are given. The tractability of the biomechanical systems allows detailed analysis of the overall dynamics of the search process. This analysis sheds light on the strong parallels with evolutionary search

Synchronisation effects on the behavioural performance and information dynamics of a simulated minimally cognitive robotic agent

Oscillatory activity is ubiquitous in nervous systems, with solid evidence that synchronisation mechanisms underpin cognitive processes. Nevertheless, its informational content and relationship with behaviour are still to be fully understood. In addition, cognitive systems cannot be properly appreciated without taking into account brain–body– environment interactions. In this paper, we developed a model based on the Kuramoto Model of coupled phase oscillators to explore the role of neural synchronisation in the performance of a simulated robotic agent in two different minimally cognitive tasks. We show that there is a statistically significant difference in performance and evolvability depending on the synchronisation regime of the network. In both tasks, a combination of information flow and dynamical analyses show that networks with a definite, but not too strong, propensity for synchronisation are more able to reconfigure, to organise themselves functionally and to adapt to different behavioural conditions. The results highlight the asymmetry of information flow and its behavioural correspondence. Importantly, it also shows that neural synchronisation dynamics, when suitably flexible and reconfigurable, can generate minimally cognitive embodied behaviour

Upravljanje robotom pomoću anticipacijskih potencijala mozga

Recently Biomedical Engineering showed advances in using brain potentials for control of physical devices, in particular, robots. This paper is focused on controlling robots using anticipatory brain potentials. An oscillatory brain potential generated in the CNV Flip-Flop Paradigm is used to trigger sequence of robot behaviors. Experimental illustration is given in which two robotic arms, driven by a brain expectancy potential oscillation, cooperatively solve the well known problem of Towers of Hanoi.U posljednje vrijeme je u području biomedicinskog inženjerstva postignut napredak u korištenju potencijala mozga za upravljanje fizičkim napravama, posebice robotima. U radu je opisana mogućnost upravljanja robotima pomoću anticipacijskih potencijala mozga. Oscilacijski potencijal mozga generiran u CNV (Contingent Negative Variation) flip-flop paradigmi se koristi za okidanje slijeda ponašanja robota. U radu je prikazana eksperimentalna ilustracija rješavanja dobro poznatog problema Hanojskih tornjeva pomoću dvije robotske ruke upravljane moždanim potencijalom očekivanja

Adaptivity through alternate freeing and freezing of degrees of freedom

Starting with fewer degrees of freedom has been shown to enable a more efficient exploration of the sensorimotor space. While not necessarily leading to optimal task performance, it results in a smaller number of directions of stability, which guide the coordination of additional degrees of freedom. The developmental release of additional degrees of freedom is then expected to allow for optimal task performance and more tolerance and adaptation to environmental interaction. In this paper, we test this assumption with a small-sized humanoid robot that learns to swing under environmental perturbations. Our experiments show that a progressive release of degrees of freedom alone is not sufficient to cope with environmental perturbations. Instead, alternate freezing and freeing of the degrees of freedom is required. Such finding is consistent with observations made during transitional periods in acquisition of skills in infants

Robot control with biological cells

At present there exists a large gap in size, performance, adaptability and robustness between natural and artificial information processors for performing coherent perception-action tasks under real-time constraints. Even the simplest organisms have an enviable capability of coping with an unknown dynamic environment. Robots, in contrast, are still clumsy if confronted with such complexity. This paper presents a bio-hybrid architecture developed for exploring an alternate approach to the control of autonomous robots. Circuits prepared from amoeboid plasmodia of the slime mold Physarum polycephalum are interfaced with an omnidirectional hexapod robot. Sensory signals from the macro-physical environment of the robot are transduced to cellular scale and processed using the unique micro-physical features of intracellular information processing. Conversely, the response form the cellular computation is amplified to yield a macroscopic output action in the environment mediated through the robot’s actuators

Efficient learning of sequential tasks for collaborative robots: a neurodynamic approach

Dissertação de mestrado integrado em Engenharia Eletrónica, Industrial e ComputadoresIn the recent years, there has been an increasing demand for collaborative robots able to interact and co operate with ordinary people in several human environments, sharing physical space and working closely with people in joint tasks, both within industrial and domestic environments. In some scenarios, these robots will come across tasks that cannot be fully designed beforehand, resulting in a need for flexibility and adaptation to the changing environments. This dissertation aims to endow robots with the ability to acquire knowledge of sequential tasks using the Programming by Demonstration (PbD) paradigm. Concretely, it extends the learning models - based on Dynamic Neural Fields (DNFs) - previously developed in the Mobile and Anthropomorphic Robotics Laboratory (MARLab), at the University of Minho, to the collaborative robot Sawyer, which is amongst the newest collaborative robots on the market. The main goal was to endow Sawyer with the ability to learn a sequential task from tutors’ demonstrations, through a natural and efficient process. The developed work can be divided into three main tasks: (1) first, a previously developed neuro-cognitive control architecture for extracting the sequential structure of a task was implemented and tested in Sawyer, combined with a Short-Term Memory (STM) mechanism to memorize a sequence in one-shot, aiming to reduce the number of demonstration trials; (2) second, the previous model was extended to incorporate workspace information and action selection in a Human-Robot Collaboration (HRC) scenario where robot and human co worker coordinate their actions to construct the structure; and (3) third, the STM mechanism was also extended to memorize ordinal and temporal aspects of the sequence, demonstrated by tutors with different behavior time scales. The models implemented contributed to a more intuitive and practical interaction with the robot for human co-workers. The STM model made the learning possible from few demonstrations to comply with the requirement of being an efficient method for learning. Moreover, the recall of the memorized information allowed Sawyer to evolve from being in a learning position to be in a teaching one, obtaining the capability of assisting inexperienced co-workers.Nos últimos anos, tem havido uma crescente procura por robôs colaborativos capazes de interagir e cooperar com pessoas comuns em vários ambientes, partilhando espaço físico e trabalhando em conjunto, tanto em ambientes industriais como domésticos. Em alguns cenários, estes robôs serão confrontados com tarefas que não podem ser previamente planeadas, o que resulta numa necessidade de existir flexibilidade e adaptação ao ambiente que se encontra em constante mudança. Esta dissertação pretende dotar robôs com a capacidade de adquirir conhecimento de tarefas sequenciais utilizando técnicas de Programação por Demonstração. De forma a continuar o trabalho desenvolvido no Laboratório de Robótica Móvel e Antropomórfica da Universidade do Minho, esta dissertação visa estender os modelos de aprendizagem previamente desenvolvidos ao robô colaborativo Sawyer, que é um dos mais recentes no mercado. O principal objetivo foi dotar o robô com a capacidade de aprender tarefas sequenciais por demonstração, através de um processo natural e eficiente. O trabalho desenvolvido pode ser dividido em três tarefas principais: (1) em primeiro lugar, uma arquitetura de controlo baseada em modelos neurocognitivos, desenvolvida anteriormente, para aprender a estrutura de uma tarefa sequencial foi implementada e testada no robô Sawyer, conjugada com um mecanismo de Short Term Memory que permitiu memorizar uma sequência apenas com uma demonstração, para reduzir o número de demonstrações necessárias; (2) em segundo lugar, o modelo anterior foi estendido para englobar informação acerca do espaço de trabalho e seleção de ações num cenário de Colaboração Humano-Robô em que ambos coordenam as suas ações para construir a tarefa; (3) em terceiro lugar, o mecanismo de Short-Term Memory foi também estendido para memorizar informação ordinal e temporal de uma sequência de passos demonstrada por tutores com comportamentos temporais diferentes. Os modelos implementados contribuíram para uma interação com o robô mais intuitiva e prática para os co-workers humanos. O mecanismo de Short-Term Memory permitiu que a aprendizagem fosse realizada a partir de poucas demonstrações, para cumprir com o requisito de ser um método de aprendizagem eficiente. Além disso, a informação memorizada permitiu ao Sawyer evoluir de uma posição de aprendizagem para uma posição em que é capaz de instruir co-workers inexperientes.This work was carried out within the scope of the project “PRODUTECH SIF - Soluções para a Indústria do Futuro”, reference POCI-01-0247-FEDER-024541, cofunded by “Fundo Europeu de Desenvolvimento Regional (FEDER)”, through “Programa Operacional Competitividade e Internacionalização (POCI)”