Acquiring moving skills in robots with evolvable morphologies: Recent results and outlook

© 2017 ACM. We construct and investigate a strongly embodied evolutionary system, where not only the controllers but also the morphologies undergo evolution in an on-line fashion. In these studies, we have been using various types of robot morphologies and controller architectures in combination with several learning algorithms, e.g. evolutionary algorithms, reinforcement learning, simulated annealing, and HyperNEAT. This hands-on experience provides insights and helps us elaborate on interesting research directions for future development

Importance of Parameter Settings on the Benefits of Robot-to-Robot Learning in Evolutionary Robotics

Robot-to-robot learning, a specific case of social learning in robotics, enables multiple robots to share learned skills while completing a task. The literature offers various statements of its benefits. Robots using this type of social learning can reach a higher performance, an increased learning speed, or both, compared to robots using individual learning only. No general explanation has been advanced for the difference in observations, which make the results highly dependent on the particular system and parameter setting. In this paper, we perform a detailed analysis into the effects of robot-to-robot learning. As a result, we show that this type of social learning can reduce the sensitivity of the learning process to the choice of parameters in two ways. First, robot-to-robot learning can reduce the number of bad performing individuals in the population. Second, robot-to-robot learning can increase the chance of having a successful run, where success is defined as the presence of a high performing individual. Additionally, we show that robot-to-robot learning results in an increased learning speed for almost all parameter settings. Our results indicate that robot-to-robot learning is a powerful mechanism which leads to benefits in both performance and learning speed

Open-Ended Evolutionary Robotics: an Information Theoretic Approach

This paper is concerned with designing self-driven fitness functions for Embedded Evolutionary Robotics. The proposed approach considers the entropy of the sensori-motor stream generated by the robot controller. This entropy is computed using unsupervised learning; its maximization, achieved by an on-board evolutionary algorithm, implements a "curiosity instinct", favouring controllers visiting many diverse sensori-motor states (sms). Further, the set of sms discovered by an individual can be transmitted to its offspring, making a cultural evolution mode possible. Cumulative entropy (computed from ancestors and current individual visits to the sms) defines another self-driven fitness; its optimization implements a "discovery instinct", as it favours controllers visiting new or rare sensori-motor states. Empirical results on the benchmark problems proposed by Lehman and Stanley (2008) comparatively demonstrate the merits of the approach

Lamarckian Evolution of Simulated Modular Robots

We study evolutionary robot systems where not only the robot brains but also the robot bodies are evolvable. Such systems need to include a learning period right after ‘birth' to acquire a controller that fits the newly created body. In this paper we investigate the possibility of bootstrapping infant robot learning through employing Lamarckian inheritance of parental controllers. In our system controllers are encoded by a combination of a morphology dependent component, a Central Pattern Generator (CPG), and a morphology independent part, a Compositional Pattern Producing Network (CPPN). This makes it possible to transfer the CPPN part of controllers between different morphologies and to create a Lamarckian system. We conduct experiments with simulated modular robots whose fitness is determined by the speed of locomotion, establish the benefits of inheriting optimized parental controllers, shed light on the conditions that influence these benefits, and observe that changing the way controllers are evolved also impacts the evolved morphologies

La misión de Jesús: Nazaret, escoge colaboradores

El autor presenta a Jesús desde la experiencia de su pueblo concreto. Luego recuerda los primeros pasos de los evangelios en orden a llevar a cabo dicha experiencia. Jesús se presenta en ese pueblo concreto a anunciar con certeza absoluta que Dios ha tomado el mundo para salvarlo. El P. Miguel dice: “El segundo aspecto que quería tocar es el de los primeros pasos que siguió a Jesús que nos presenta los evangelios. Es algo a lo que ya uno se acostumbra, pero es algo insospechado: es buscar colaboradores; siendo Hijo de Dios, busca colaboradores. Así nos lo presentan los evangelios en Mc 1, 16-20 y 2, 13-14; Jn 1, 38-43"

Adaptation of Robot Behaviour through Online Evolution and Neuromodulated Learning

Abstract. We propose and evaluate a novel approach to the online syn-thesis of neural controllers for autonomous robots. We combine online evolution of weights and network topology with neuromodulated learn-ing. We demonstrate our method through a series of simulation-based ex-periments in which an e-puck-like robot must perform a dynamic concur-rent foraging task. In this task, scattered food items periodically change their nutritive value or become poisonous. Our results show that when neuromodulated learning is employed, neural controllers are synthesised faster than by evolution alone. We demonstrate that the online evolu-tionary process is capable of generating controllers well adapted to the periodic task changes. An analysis of the evolved networks shows that they are characterised by specialised modulatory neurons that exclusively regulate the output neurons

溜池地帯における共同田植と水利 : 兵庫県多紀郡沢田部落における事例的研究

textabstractClimbing fibers (CFs) originating in the inferior olive (IO) constitute one of the main inputs to the cerebellum. In the mammalian cerebellar cortex each of them climbs into the dendritic tree of up to ten Purkinje cells where they make hundreds of synaptic contacts and elicit the socalled all-or-none complex spikes controlling the output. While it has been proven that CFs contact molecular layer interneurons (MLIs) via spillover mechanisms, it remains to be elucidated to what extent CFs contact the main type of interneuron in the granular layer, i.e. the Golgi cells (GoCs). This issue is particularly relevant, because direct contacts would imply that CFs can also control computations at the input stage of the cerebellar cortical network. Here, we performed a systematic morphological investigation of labeled CFs and GoCs at the light microscopic level following their path and localization through the neuropil in both the granular and molecular layer. Whereas the appositions of CFs to Purkinje cells, stellate cells and basket cells in the molecular layer were prominent and numerous, those to cell-bodies and dendrites of GoCs in both the granular layer and molecular layer were virtually absent. Our results argue against the functional significance of direct synaptic contacts between CFs and interneurons at the input stage, but support those at the output stage