Dynamic Perception after Visually Guided Grasping by a Human-Like Autonomous Robot

We will explore dynamic perception following the visually guided grasping of several objects by a human-like autonomous robot. This competency serves for object categorization. Physical interaction with the hand-held object gives the neural network of the robot the rich, coherent and multi-modal sensory input. Multi-layered self-organizing maps are designed and examined in static and dynamic conditions. The results of the tests in the former condition show its capability of robust categorization against noise. The network also shows better performance than a single-layered map does. In the latter condition we focus on shaking behavior by moving only the forearm of the robot. In some combinations of grasping style and shaking radius the network is capable of categorizing two objects robustly. The results show that the network capability to achieve the task largely depends on how to grasp and how to move the objects. These results together with a preliminary simulation are promising toward the self-organization of a high degree of autonomous dynamic object categorization.

Slow dynamics in structured neural network models

Samuel Pavio Muscinelli

Humans and some other animals are able to perform tasks that require coordination of movements across multiple temporal scales, ranging from hundreds of milliseconds to several seconds. The fast timescale at which neurons naturally operate, on the order of tens of milliseconds, is well-suited to support motor control of rapid movements. In contrast, to coordinate movements on the order of seconds, a neural network should produce reliable dynamics on a similarly âslowâ timescale. Neurons and synapses exhibit biophysical mechanisms whose timescales range from tens of milliseconds to hours, which suggests a possible role of these mechanisms in producing slow reliable dynamics. However, how such mechanisms influence network dynamics is not yet understood. An alternative approach to achieve slow dynamics in a neural network consists in modifying its connectivity structure. Still, the limitations of this approach and in particular to what degree the weights require fine-tuning, remain unclear. Understanding how both the single neuron mechanisms and the connectivity structure might influence the network dynamics to produce slow timescales is the main goal of this thesis. We first consider the possibility of obtaining slow dynamics in binary networks by tuning their connectivity. It is known that binary networks can produce sequential dynamics. However, if the sequences consist of random patterns, the typical length of the longest sequence that can be produced grows linearly with the number of units. Here, we show that we can overcome this limitation by carefully designing the sequence structure. More precisely, we obtain a constructive proof that allows to obtain sequences whose length scales exponentially with the number of units. To achieve this however, one needs to exponentially fine-tune the connectivity matrix. Next, we focus on the interaction between single neuron mechanisms and recurrent dynamics. Particular attention is dedicated to adaptation, which is known to have a broad range of timescales and is therefore particularly interesting for the subject of this thesis. We study the dynamics of a random network with adaptation using mean-field techniques, and we show that the network can enter a state of resonant chaos. Interestingly, the resonance frequency of this state is independent of the connectivity strength and depends only on the properties of the single neuron model. The approach used to study networks with adaptation can also be applied when considering linear rate units with an arbitrary number of auxiliary variables. Based on a qualitative analysis of the mean-field theory for a random network whose neurons are described by a D -dimensional rate model, we conclude that the statistics of the chaotic dynamics are strongly influenced by the single neuron model under investigation. Using a reservoir computing approach, we show preliminary evidence that slow adaptation can be beneficial when performing tasks that require slow timescales. The positive impact of adaptation on the network performance is particularly strong in the presence of noise. Finally, we propose a network architecture in which the slowing-down effect due to adaptation is combined with a hierarchical structure, with the purpose of efficiently generate sequences that require multiple, hierarchically organized timescales.

EPFL2018

Evolutionary synthesis of analog networks

Claudio Mattiussi

The significant increase in the available computational power that took place in recent decades has been accompanied by a growing interest in the application of the evolutionary approach to the synthesis of many kinds of systems and, in particular, to the synthesis of systems like analog electronic circuits, neural networks, and, more generally, autonomous systems, for which no satisfying systematic and general design methodology has been found to date. Despite some interesting results in the evolutionary synthesis of these kinds of systems, the endowment of an artificial evolutionary process with the potential for an appreciable increase of complexity of the systems thus generated appears still an open issue. In this thesis the problem of the evolutionary growth of complexity is addressed taking as starting point the insights contained in the published material reporting the unfinished work done in the late 1940s and early 1950s by John von Neumann on the theory of self-reproducing automata. The evolutionary complexity-growth conditions suggested in that work are complemented here with a series of auxiliary conditions inspired by what has been discovered since then relatively to the structure of biological systems, with a particular emphasis on the workings of genetic regulatory networks seen as the most elementary, full-fledged level of organization of existing living organisms. In this perspective, the first chapter is devoted to the formulation of the problem of the evolutionary growth of complexity, going from the description of von Neumann's complexity-growth conditions to the specification of a set of auxiliary complexity-growth conditions derived from the analysis of the operation of genetic regulatory networks. This leads to the definition of a particular structure for the kind of systems that will be evolved and to the specification of the genetic representation for them. A system with the required structure — for which the name analog network is suggested — corresponds to a collection of devices whose terminals are connected by links characterized by a scalar value of interaction strength. One of the specificities of the evolutionary system defined in this thesis is the way these values of interaction strength are determined. This is done by associating with each device terminal of the evolving analog network a sequence of characters extracted from the sequences that constitute the genome representing the network, and by defining a map from pairs of sequences of characters to values of interaction strength. Whereas the first chapter gives general prescriptions for the definition of an evolutionary system endowed with the desired complexity-growth potential, the second chapter is devoted to the specification of all the details of an actual implementation of those prescriptions. In this chapter the structure of the genome and of the corresponding genetic operators are defined. A technique for the genetic encoding of the devices constituting the analog network is described, along with a way to implement the map that specifies the interaction between the devices of the evolved system, and between them and the devices constituting the external environment of the evolved system. The proposed implementation of the interaction map is based on the local alignment of sequences of characters. It is shown how the parameters defining the local alignment can be chosen, and what strategies can be adopted to prevent the proliferation of unwanted interactions. The third chapter is devoted to the application of the evolutionary system defined in the second chapter to problems aimed at assessing the suitability in an evolutionary context of the local alignment technique and to problems aimed at assessing the evolutionary potential of the complete evolutionary system when applied to the synthesis of analog networks. Finally, the fourth chapter briefly considers some further questions that are relevant to the proposed approach but could not be addressed in the context of this thesis. A series of appendixes is devoted to some complementary issues: the definition of a measure of diversity for an evolutionary population employing the genetic description introduced in this thesis; the choice of the quantizer for the values of interaction strength between the devices constituting the evolved analog network; the modifications required to use the analog electronic circuit simulator SPICE as a simulation engine for an evolutionary or an optimization process.

EPFL2005

Evolutionary Robotics. The Biology, Intelligence, and Technology of Self-organizing Machines

Dario Floreano

Evolutionary robotics is a new technique for the automatic creation of autonomous robots. Inspired by the Darwinian principle of selective reproduction of the fittest, it views robots as autonomous artificial organisms that develop their own skills in close interaction with the environment and without human intervention. Drawing heavily on biology and ethology, it uses the tools of neural networks, genetic algorithms, dynamic systems, and biomorphic engineering. The resulting robots share with simple biological systems the characteristics of robustness, simplicity, small size, flexibility, and modularity. In evolutionary robotics, an initial population of artificial chromosomes, each encoding the control system of a robot, is randomly created and put into the environment. Each robot is then free to act (move, look around, manipulate) according to its genetically specified controller while its performance on various tasks is automatically evaluated. The fittest robots then "reproduce" by swapping parts of their genetic material with small random mutations. The process is repeated until the "birth" of a robot that satisfies the performance criteria. This book describes the basic concepts and methodologies of evolutionary robotics and the results achieved so far. An important feature is the clear presentation of a set of empirical experiments of increasing complexity. Software with a graphic interface, freely available on a Web page, will allow the reader to replicate and vary (in simulation and on real robots) most of the experiments.