Some Experiments on the influence of Problem Hardness in Morphological Development based Learning of Neural Controllers

Natural beings undergo a morphological development process of their bodies while they are learning and adapting to the environments they face from infancy to adulthood. In fact, this is the period where the most important learning pro-cesses, those that will support learning as adults, will take place. However, in artificial systems, this interaction between morphological development and learning, and its possible advantages, have seldom been considered. In this line, this paper seeks to provide some insights into how morphological development can be harnessed in order to facilitate learning in em-bodied systems facing tasks or domains that are hard to learn. In particular, here we will concentrate on whether morphological development can really provide any advantage when learning complex tasks and whether its relevance towards learning in-creases as tasks become harder. To this end, we present the results of some initial experiments on the application of morpho-logical development to learning to walk in three cases, that of a quadruped, a hexapod and that of an octopod. These results seem to confirm that as task learning difficulty increases the application of morphological development to learning becomes more advantageous.Comment: 10 pages, 4 figure

Dynamical modeling of collective behavior from pigeon flight data: flock cohesion and dispersion

Several models of flocking have been promoted based on simulations with qualitatively naturalistic behavior. In this paper we provide the first direct application of computational modeling methods to infer flocking behavior from experimental field data. We show that this approach is able to infer general rules for interaction, or lack of interaction, among members of a flock or, more generally, any community. Using experimental field measurements of homing pigeons in flight we demonstrate the existence of a basic distance dependent attraction/repulsion relationship and show that this rule is sufficient to explain collective behavior observed in nature. Positional data of individuals over time are used as input data to a computational algorithm capable of building complex nonlinear functions that can represent the system behavior. Topological nearest neighbor interactions are considered to characterize the components within this model. The efficacy of this method is demonstrated with simulated noisy data generated from the classical (two dimensional) Vicsek model. When applied to experimental data from homing pigeon flights we show that the more complex three dimensional models are capable of predicting and simulating trajectories, as well as exhibiting realistic collective dynamics. The simulations of the reconstructed models are used to extract properties of the collective behavior in pigeons, and how it is affected by changing the initial conditions of the system. Our results demonstrate that this approach may be applied to construct models capable of simulating trajectories and collective dynamics using experimental field measurements of herd movement. From these models, the behavior of the individual agents (animals) may be inferred

Towards Growing Robots: A Piecewise Morphology-Controller Co-adaptation Strategy for Legged Locomotion

Control of robots has largely been based on the assumption of a fixed morphology. Accordingly, robot designs have been stationary in time, except for the case of modular robots. Any drastic change in morphology, hence, requires a remodelling of the controller. This work takes inspiration from developmental robotics to present a piecewise morphology-controller growth/adaptation strategy that facilitates fast and reliable control adaptation to growing robots. We demonstrate our methodology on a simple 3 degree of freedom walking robot with adjustable foot lengths and with varying inertial conditions. Our results show not only the effectiveness and reliability of the piecewise morphology controller co-adaptation (PMCCA) strategy, but also highlight the need for morphological adaptation as a robot design strategy

Robots that can adapt like animals

As robots leave the controlled environments of factories to autonomously function in more complex, natural environments, they will have to respond to the inevitable fact that they will become damaged. However, while animals can quickly adapt to a wide variety of injuries, current robots cannot "think outside the box" to find a compensatory behavior when damaged: they are limited to their pre-specified self-sensing abilities, can diagnose only anticipated failure modes, and require a pre-programmed contingency plan for every type of potential damage, an impracticality for complex robots. Here we introduce an intelligent trial and error algorithm that allows robots to adapt to damage in less than two minutes, without requiring self-diagnosis or pre-specified contingency plans. Before deployment, a robot exploits a novel algorithm to create a detailed map of the space of high-performing behaviors: This map represents the robot's intuitions about what behaviors it can perform and their value. If the robot is damaged, it uses these intuitions to guide a trial-and-error learning algorithm that conducts intelligent experiments to rapidly discover a compensatory behavior that works in spite of the damage. Experiments reveal successful adaptations for a legged robot injured in five different ways, including damaged, broken, and missing legs, and for a robotic arm with joints broken in 14 different ways. This new technique will enable more robust, effective, autonomous robots, and suggests principles that animals may use to adapt to injury

Turing learning: : A metric-free approach to inferring behavior and its application to swarms

We propose Turing Learning, a novel system identification method for inferring the behavior of natural or artificial systems. Turing Learning simultaneously optimizes two populations of computer programs, one representing models of the behavior of the system under investigation, and the other representing classifiers. By observing the behavior of the system as well as the behaviors produced by the models, two sets of data samples are obtained. The classifiers are rewarded for discriminating between these two sets, that is, for correctly categorizing data samples as either genuine or counterfeit. Conversely, the models are rewarded for 'tricking' the classifiers into categorizing their data samples as genuine. Unlike other methods for system identification, Turing Learning does not require predefined metrics to quantify the difference between the system and its models. We present two case studies with swarms of simulated robots and prove that the underlying behaviors cannot be inferred by a metric-based system identification method. By contrast, Turing Learning infers the behaviors with high accuracy. It also produces a useful by-product - the classifiers - that can be used to detect abnormal behavior in the swarm. Moreover, we show that Turing Learning also successfully infers the behavior of physical robot swarms. The results show that collective behaviors can be directly inferred from motion trajectories of individuals in the swarm, which may have significant implications for the study of animal collectives. Furthermore, Turing Learning could prove useful whenever a behavior is not easily characterizable using metrics, making it suitable for a wide range of applications.Comment: camera-ready versio

A Theory of Cheap Control in Embodied Systems

We present a framework for designing cheap control architectures for embodied agents. Our derivation is guided by the classical problem of universal approximation, whereby we explore the possibility of exploiting the agent's embodiment for a new and more efficient universal approximation of behaviors generated by sensorimotor control. This embodied universal approximation is compared with the classical non-embodied universal approximation. To exemplify our approach, we present a detailed quantitative case study for policy models defined in terms of conditional restricted Boltzmann machines. In contrast to non-embodied universal approximation, which requires an exponential number of parameters, in the embodied setting we are able to generate all possible behaviors with a drastically smaller model, thus obtaining cheap universal approximation. We test and corroborate the theory experimentally with a six-legged walking machine. The experiments show that the sufficient controller complexity predicted by our theory is tight, which means that the theory has direct practical implications. Keywords: cheap design, embodiment, sensorimotor loop, universal approximation, conditional restricted Boltzmann machineComment: 27 pages, 10 figure

Diversity of supernovae Ia determined using equivalent widths of Si II 4000

Spectroscopic and photometric properties of low and high-z supernovae Ia (SNe Ia) have been analyzed in order to achieve a better understanding of their diversity and to identify possible SN Ia sub-types. We use wavelet transformed spectra in which one can easily measure spectral features. We investigate the \ion{Si}{II} 4000 equivalent width (EW_w\lbrace\ion{Si}{II}\rbrace). The ability and, especially, the ease in extending the method to SNe at high-$z$ is demonstrated. We applied the method to 110 SNe Ia and found correlations between EW_w\lbrace\ion{Si}{II}\rbrace and parameters related to the light-curve shape for 88 supernovae with available photometry. No evidence for evolution of EW_w\lbrace\ion{Si}{II}\rbrace with redshift is seen. Three sub-classes of SNe Ia were confirmed using an independent cluster analysis with only light-curve shape, colour, and EW_w\lbrace\ion{Si}{II}\rbrace. SNe from high-$z$ samples seem to follow a similar grouping to nearby objects. The EW_w\lbrace\ion{Si}{II}\rbrace value measured on a single spectrum may point towards SN Ia sub-classification, avoiding the need for expansion velocity gradient calculations.Comment: 12 pages, 5 figure

Genome variations: Effects on the robustness of neuroevolved control for swarm robotics systems

Manual design of self-organized behavioral control for swarms of robots is a complex task. Neuroevolution has proved a viable alternative given its capacity to automatically synthesize controllers. In this paper, we introduce the concept of Genome Variations (GV) in the neuroevolution of behavioral control for robotic swarms. In an evolutionary setup with GV, a slight mutation is applied to the evolving neural network parameters before they are copied to the robots in a swarm. The genome variation is individual to each robot, thereby generating a slightly heterogeneous swarm. GV represents a novel approach to the evolution of robust behaviors, expected to generate more stable and robust individual controllers, and bene t swarm behaviors that can deal with small heterogeneities in the behavior of other members in the swarm. We conduct experiments using an aggregation task, and compare the evolved solutions to solutions evolved under ideal, noise-free conditions, and to solutions evolved with traditional sensor noise.info:eu-repo/semantics/acceptedVersio

Berkeley Supernova Ia Program II: Initial Analysis of Spectra Obtained Near Maximum Brightness

In this second paper in a series we present measurements of spectral features of 432 low-redshift (z < 0.1) optical spectra of 261 Type Ia supernovae (SNe Ia) within 20 d of maximum brightness. The data were obtained from 1989 through the end of 2008 as part of the Berkeley SN Ia Program (BSNIP) and are presented in BSNIP I (Silverman et al. 2012). We describe in detail our method of automated, robust spectral feature definition and measurement which expands upon similar previous studies. Using this procedure, we attempt to measure expansion velocities, pseudo-equivalent widths (pEW), spectral feature depths, and fluxes at the centre and endpoints of each of nine major spectral feature complexes. We investigate how velocity and pEW evolve with time and how they correlate with each other. Various spectral classification schemes are employed and quantitative spectral differences among the subclasses are investigated. Several ratios of pEW values are calculated and studied. The so-called Si II ratio, often used as a luminosity indicator (Nugent et al. 1995), is found to be well correlated with the so-called "SiFe" ratio and anticorrelated with the analogous "SSi ratio," confirming the results of previous studies. Furthermore, SNe Ia that show strong evidence for interaction with circumstellar material or an aspherical explosion are found to have the largest near-maximum expansion velocities and pEWs, possibly linking extreme values of spectral observables with specific progenitor or explosion scenarios. [Abridged]Comment: 73 pages, 18 figures, 15 tables, revised version re-submitted to MNRAS. Measured values of the spectral features (i.e., Appendix B) will be publicly available when the paper is accepte