Neural preprocessing and control of reactive walking machines

Research in the domain of biologically inspired walking machines has focused for the most part on the mechanical designs and locomotion control. Although some of this research has been concentrated on the generation of a reactive behavior of walking machines, it has been restricted only to a few of such reactive behaviors. However, from this research, there are only few examples where different behaviors have been implemented in one machine at the same time. In general, these walking machines were solely designed for pure locomotion, i.e. without sensing environmental stimuli. <p> Therefore, in this thesis, biologically inspired walking machines with different reactive behaviors are presented. Inspired by obstacle avoidance and escape behavior of scorpions and cockroaches, such behavior is implemented in the walking machines as a negative tropism. On the other hand, a sound induced behavior called “sound tropism”, in analogy to the prey capture behavior of spiders, is employed as a model of a positive tropism. The biological sensing systems which those animals use to trigger the described behaviors are investigated so that they can be reproduced in the abstract form with respect to their principle functionalities. In addition, the morphologies of a salamander and a cockroach which are designed for efficient locomotion are also taken into account for the leg and trunk designs of the four- and six-legged walking machines, respectively. <p> Different behavior controls for generating the biologically inspired reactive behaviors are developed on the basis of a modular neural structure. Each behavior control consists of a neural preprocessing module and a neural control module. Preprocessing is for sensory signals while the neural control generates basic locomotion and changes the appropriate motions, e.g. turning left, right or walking backward, with respect to sensory signals. Neural preprocessing and control are formed by realizing discrete-time dynamical properties of recurrent neural networks. Parts of the networks are generated and optimized by using an evolutionary algorithm. Utilizing the modular neural structure, the coupling of the neural control module with different neural preprocessing modules leads to the desired behavior controllers, e.g. obstacle avoidance and sound tropism. Furthermore, these behavior controllers are then fused by using a sensor fusion technique consisting of lookup table and time scheduling methods to obtain an effective behavior fusion controller, whereby different neural preprocessing modules have to cooperate. <p> Eventually, all of these reactive behavior controllers together with the physical sensor systems are implemented on the physical walking machines to be tested in a real world environment. The fully equipped walking machines can be seen as artificial perception-action systems. As a result, the walking machine(s) is able to respond to environmental stimuli, e.g. wandering around, sound tropism (positive tropism), avoiding obstacles and even escaping from corners as well as deadlock situations (negative tropism). The developed controller is universal in the sense that it can be implemented on different types of walking machines, e.g. four- and six-legged walking machines, giving comparably good results without changing parameters.Im Bereich biologisch inspirierter Laufmaschinen konzentrierte sich die Forschung meist auf die reine Bewegungskontrolle sowie das mechanische Design. Obwohl ein Teil dieser Forschung sich auch mit der Erzeugung reaktiver Verhaltensweisen von Robotern beschäftigte, war dies auf einige wenige reaktive Verhaltensweisen beschränkt; und zwar war auf einem Roboter nur jeweils eine Verhaltensweise implementiert. Es gibt nur wenige Ansätze, die sich mit der Erzeugung mehrerer reaktiver Verhaltensweisen einer Maschine gleichzeitig beschäftigen. Im Allgemeinen wurden Laufmaschinen nur zum Zwecke der reinen Fortbewegung konzipiert, d.h. ohne dass sie ihre Umgebung wahrnehmen konnten. <p> Diese Arbeit stellt biologisch inspirierte Laufmaschinen vor, welche mehrere verschiedene reaktive Verhaltensweisen zeigen. Inspiriert vom Hindernisvermeidungs- und Fluchtverhalten der Skorpione und Kakerlaken wird ein solches Verhalten in der Laufmaschine mittels eines negativen Tropismus erzeugt. Andererseits wird ein akustisch motiviertes Verhalten, ein sog. “akustischer Tropismus” (Sound Tropism), in Analogie zum Jagdverhalten von Spinnen, als Beispiel eines positiven Tropismus angewendet. Um die oben beschriebenen Verhalten in abstrahierter Weise reproduzieren zu können, wird außerdem der biologische Wahrnehmungsapparat der genannten Tiere im Hinblick auf ihre prinzipielle Funktionalität untersucht. Zusätzlich werden die Morphologien von Salamander und Kakerlake, welche für effiziente Bewegung gebaut sind, für die Bein- und Körpergestaltung in Betracht gezogen. <p> Basierend auf einem modularen neuronalen Modell werden verschiedene Verhaltenskontroller für die Erzeugung biologisch inspirierter reaktiver Verhaltensweisen entwickelt. Jede Verhaltenskontrolle besteht aus neuronalen Signal-Vorverarbeitungseinheiten und Kontrollmodulen. Für die Vorverarbeitung sensorischer Signale werden rekurrente neuronale Netze genutzt, ebenso wie für die Kontrolle und die Erzeugung von Laufbewegungen, sowie der Änderung der Bewegung, z.B. Drehung nach rechts, links oder rückwärts, in Abhängigkeit von Sensorsignalen. Die effektive neuronale Verarbeitung und Kontrolle wird erreicht durch Ausnutzung der dynamisschen Eigenschaften der rekurrenten neuronalen Netze, die zum Teil durch evolutionäre Algorithmen konstruiert bzw. optimiert wurden. Den modularen Aufbau nutzend führt eine Kombination der verschiedenen neuronalen Verarbeitungseinheiten zu den gewünschten Verhaltenssteuerungen. Des weiteren werden diese Verhaltenssteuerungen zusammengeführt mittels einer Sensor-Fusions-Technik, welche aus Tabellen- und “Time-Scheduling” -Methoden besteht. Damit entsteht letztlich eine neue effektive verhaltenfusionierte Steuerung, die sich auf verschiedenste Laufmaschinen übertragen läßt. <p> Abschließend werden alle diese reaktiven Verhaltenssteuerungen zusammen mit einem Sensorsystem in physikalischen Laufmaschinen implementiert, um sie zu testen und als künstliche Perzeptions-Aktions-Maschine zu demonstrieren. Es wird gezeigt, dass die Laufmaschinen in der Lage sind in der Umgebung umherzuwandern und auf Reize der Umgebung zu reagieren, z.B. durch akustischen Tropismus (positiver Tropismus), durch Hindernisvermeidung und sogar durch Entkommen aus Ecken und Sackgassen (negativer Tropismus). Der entwickelte Kontroller ist universell in dem Sinne, dass er auf Laufmaschinen mit unterschiedlicher Beinanzahl, hier vier und sechs Beine, ohne Parameteranpassung mit vergleichbaren Ergebnissen implentiert werden kann

Multiple chaotic central pattern generators with learning for legged locomotion and malfunction compensation

An originally chaotic system can be controlled into various periodic dynamics. When it is implemented into a legged robot's locomotion control as a central pattern generator (CPG), sophisticated gait patterns arise so that the robot can perform various walking behaviors. However, such a single chaotic CPG controller has difficulties dealing with leg malfunction. Specifically, in the scenarios presented here, its movement permanently deviates from the desired trajectory. To address this problem, we extend the single chaotic CPG to multiple CPGs with learning. The learning mechanism is based on a simulated annealing algorithm. In a normal situation, the CPGs synchronize and their dynamics are identical. With leg malfunction or disability, the CPGs lose synchronization leading to independent dynamics. In this case, the learning mechanism is applied to automatically adjust the remaining legs' oscillation frequencies so that the robot adapts its locomotion to deal with the malfunction. As a consequence, the trajectory produced by the multiple chaotic CPGs resembles the original trajectory far better than the one produced by only a single CPG. The performance of the system is evaluated first in a physical simulation of a quadruped as well as a hexapod robot and finally in a real six-legged walking machine called AMOSII. The experimental results presented here reveal that using multiple CPGs with learning is an effective approach for adaptive locomotion generation where, for instance, different body parts have to perform independent movements for malfunction compensation.Comment: 48 pages, 16 figures, Information Sciences 201

Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot

Walking animals, like insects, with little neural computing can effectively perform complex behaviors. They can walk around their environment, escape from corners/deadlocks, and avoid or climb over obstacles. While performing all these behaviors, they can also adapt their movements to deal with an unknown situation. As a consequence, they successfully navigate through their complex environment. The versatile and adaptive abilities are the result of an integration of several ingredients embedded in their sensorimotor loop. Biological studies reveal that the ingredients include neural dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a walking robot is a challenging task. In this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural mechanisms with plasticity, sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent network consisting of two fully connected neurons. Online correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors) in the network to generate different turning angles with short-term memory for a biomechanical walking robot. The turning information is transmitted as descending steering signals to the locomotion control which translates the signals into motor actions. As a result, the robot can walk around and adapt its turning angle for avoiding obstacles in different situations as well as escaping from sharp corners or deadlocks. Using backbone joint control embedded in the locomotion control allows the robot to climb over small obstacles. Consequently, it can successfully explore and navigate in complex environments

The implications of embodiment for behavior and cognition: animal and robotic case studies

In this paper, we will argue that if we want to understand the function of the brain (or the control in the case of robots), we must understand how the brain is embedded into the physical system, and how the organism interacts with the real world. While embodiment has often been used in its trivial meaning, i.e. 'intelligence requires a body', the concept has deeper and more important implications, concerned with the relation between physical and information (neural, control) processes. A number of case studies are presented to illustrate the concept. These involve animals and robots and are concentrated around locomotion, grasping, and visual perception. A theoretical scheme that can be used to embed the diverse case studies will be presented. Finally, we will establish a link between the low-level sensory-motor processes and cognition. We will present an embodied view on categorization, and propose the concepts of 'body schema' and 'forward models' as a natural extension of the embodied approach toward first representations.Comment: Book chapter in W. Tschacher & C. Bergomi, ed., 'The Implications of Embodiment: Cognition and Communication', Exeter: Imprint Academic, pp. 31-5

Exploring New Horizons in Evolutionary Design of Robots

International audienceThis introduction paper to the 2009 IROS workshop “Exploring new horizons in Evolutionary Design of Robots” considers the field of Evolutionary Robotics (ER) from the perspective of its potential users: roboticists. The core hypothesis motivating this field of research will be discussed, as well as the potential use of ER in a robot design process. Three main aspects of ER will be presented: (a) ER as an automatic parameter tuning procedure, which is the most mature application and is used to solve real robotics problem, (b) evolutionary-aided design, which may benefit the designer as an efficient tool to build robotic systems and (c) automatic synthesis, which corresponds to the automatic design of a mechatronic device. Critical issues will also be presented as well as current trends and pespectives in ER

Credit assignment in multiple goal embodied visuomotor behavior

The intrinsic complexity of the brain can lead one to set aside issues related to its relationships with the body, but the field of embodied cognition emphasizes that understanding brain function at the system level requires one to address the role of the brain-body interface. It has only recently been appreciated that this interface performs huge amounts of computation that does not have to be repeated by the brain, and thus affords the brain great simplifications in its representations. In effect the brain’s abstract states can refer to coded representations of the world created by the body. But even if the brain can communicate with the world through abstractions, the severe speed limitations in its neural circuitry mean that vast amounts of indexing must be performed during development so that appropriate behavioral responses can be rapidly accessed. One way this could happen would be if the brain used a decomposition whereby behavioral primitives could be quickly accessed and combined. This realization motivates our study of independent sensorimotor task solvers, which we call modules, in directing behavior. The issue we focus on herein is how an embodied agent can learn to calibrate such individual visuomotor modules while pursuing multiple goals. The biologically plausible standard for module programming is that of reinforcement given during exploration of the environment. However this formulation contains a substantial issue when sensorimotor modules are used in combination: The credit for their overall performance must be divided amongst them. We show that this problem can be solved and that diverse task combinations are beneficial in learning and not a complication, as usually assumed. Our simulations show that fast algorithms are available that allot credit correctly and are insensitive to measurement noise