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

Amyotrophic lateral sclerosis (ALS)-associated VAPB-P56S inclusions represent an ER quality control compartment

BACKGROUND: Protein aggregation and the formation of intracellular inclusions are a central feature of many neurodegenerative disorders, but precise knowledge about their pathogenic role is lacking in most instances. Here we have characterized inclusions formed in transgenic mice carrying the P56S mutant form of VAPB that causes various motor neuron syndromes including ALS8.RESULTS: Inclusions in motor neurons of VAPB-P56S transgenic mice are characterized by the presence of smooth ER-like tubular profiles, and are immunoreactive for factors that operate in the ER associated degradation (ERAD) pathway, including p97/VCP, Derlin-1, and the ER membrane chaperone BAP31. The presence of these inclusions does not correlate with signs of axonal and neuronal degeneration, and axotomy leads to their gradual disappearance, indicating that they represent reversible structures. Inhibition of the proteasome and knockdown of the ER membrane chaperone BAP31 increased the size of mutant VAPB inclusions in primary neuron cultures, while knockdown of TEB4, an ERAD ubiquitin-protein ligase, reduced their size. Mutant VAPB did not codistribute with mutant forms of seipin that are associated with an autosomal dominant motor neuron disease, and accumulate in a protective ER derived compartment termed ERPO (ER protective organelle) in neurons.CONCLUSIONS: The data indicate that the VAPB-P56S inclusions represent a novel reversible ER quality control compartment that is formed when the amount of mutant VAPB exceeds the capacity of the ERAD pathway and that isolates misfolded and aggregated VAPB from the rest of the ER. The presence of this quality control compartment reveals an additional level of flexibility of neurons to cope with misfolded protein stress in the ER

Fast Evolutionary Adaptation for Monte Carlo Tree Search

This paper describes a new adaptive Monte Carlo Tree Search (MCTS) algorithm that uses evolution to rapidly optimise its performance. An evolutionary algorithm is used as a source of control parameters to modify the behaviour of each iteration (i.e. each simulation or roll-out) of the MCTS algorithm; in this paper we largely restrict this to modifying the behaviour of the random default policy, though it can also be applied to modify the tree policy