Self-organized criticality, plasticity and sensorimotor coupling. Explorations with a neurorobotic model in a behavioural preference task

During the last two decades, analysis of 1/ƒ noise in cognitive science has led to a consider- able progress in the way we understand the organization of our mental life. However, there is still a lack of specific models providing explanations of how 1/ƒ noise is generated in cou- pled brain-body-environment systems, since existing models and experiments typically tar- get either externally observable behaviour or isolated neuronal systems but do not address the interplay between neuronal mechanisms and sensorimotor dynamics. We present a conceptual model of a minimal neurorobotic agent solving a behavioural task that makes it possible to relate mechanistic (neurodynamic) and behavioural levels of description. The model consists of a simulated robot controlled by a network of Kuramoto oscillators with ho- meostatic plasticity and the ability to develop behavioural preferences mediated by sensori- motor patterns. With only three oscillators, this simple model displays self-organized criticality in the form of robust 1/ƒ noise and a wide multifractal spectrum. We show that the emergence of self-organized criticality and 1/ƒ noise in our model is the result of three simul- taneous conditions: a) non-linear interaction dynamics capable of generating stable collec- tive patterns, b) internal plastic mechanisms modulating the sensorimotor flows, and c) strong sensorimotor coupling with the environment that induces transient metastable neuro- dynamic regimes. We carry out a number of experiments to show that both synaptic plastici- ty and strong sensorimotor coupling play a necessary role, as constituents of self-organized criticality, in the generation of 1/ƒ noise. The experiments also shown to be useful to test the robustness of 1/ƒ scaling comparing the results of different techniques. We finally discuss the role of conceptual models as mediators between nomothetic and mechanistic models and how they can inform future experimental research where self-organized critically in- cludes sensorimotor coupling among the essential interaction-dominant process giving rise to 1/ƒ noise

Serious Gaming for Test & Evaluation of Clean-Slate (Ab Initio) National Airspace System (NAS) Designs

Incremental approaches to air transportation system development inherit current architectural constraints, which, in turn, place hard bounds on system capacity, efficiency of performance, and complexity. To enable airspace operations of the future, a clean-slate (ab initio) airspace design(s) must be considered. This ab initio National Airspace System (NAS) must be capable of accommodating increased traffic density, a broader diversity of aircraft, and on-demand mobility. System and subsystem designs should scale to accommodate the inevitable demand for airspace services that include large numbers of autonomous Unmanned Aerial Vehicles and a paradigm shift in general aviation (e.g., personal air vehicles) in addition to more traditional aerial vehicles such as commercial jetliners and weather balloons. The complex and adaptive nature of ab initio designs for the future NAS requires new approaches to validation, adding a significant physical experimentation component to analytical and simulation tools. In addition to software modeling and simulation, the ability to exercise system solutions in a flight environment will be an essential aspect of validation. The NASA Langley Research Center (LaRC) Autonomy Incubator seeks to develop a flight simulation infrastructure for ab initio modeling and simulation that assumes no specific NAS architecture and models vehicle-to-vehicle behavior to examine interactions and emergent behaviors among hundreds of intelligent aerial agents exhibiting collaborative, cooperative, coordinative, selfish, and malicious behaviors. The air transportation system of the future will be a complex adaptive system (CAS) characterized by complex and sometimes unpredictable (or unpredicted) behaviors that result from temporal and spatial interactions among large numbers of participants. A CAS not only evolves with a changing environment and adapts to it, it is closely coupled to all systems that constitute the environment. Thus, the ecosystem that contains the system and other systems evolves with the CAS as well. The effects of the emerging adaptation and co-evolution are difficult to capture with only combined mathematical and computational experimentation. Therefore, an ab initio flight simulation environment must accommodate individual vehicles, groups of self-organizing vehicles, and large-scale infrastructure behavior. Inspired by Massively Multiplayer Online Role Playing Games (MMORPG) and Serious Gaming, the proposed ab initio simulation environment is similar to online gaming environments in which player participants interact with each other, affect their environment, and expect the simulation to persist and change regardless of any individual player's active participation

Recent Advances in Multi Robot Systems

To design a team of robots which is able to perform given tasks is a great concern of many members of robotics community. There are many problems left to be solved in order to have the fully functional robot team. Robotics community is trying hard to solve such problems (navigation, task allocation, communication, adaptation, control, ...). This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field. It is focused on the challenging issues of team architectures, vehicle learning and adaptation, heterogeneous group control and cooperation, task selection, dynamic autonomy, mixed initiative, and human and robot team interaction. The book consists of 16 chapters introducing both basic research and advanced developments. Topics covered include kinematics, dynamic analysis, accuracy, optimization design, modelling, simulation and control of multi robot systems

Interaction dynamics and autonomy in cognitive systems

The concept of autonomy is of crucial importance for understanding life and cognition. Whereas cellular and organismic autonomy is based in the self-production of the material infrastructure sustaining the existence of living beings as such, we are interested in how biological autonomy can be expanded into forms of autonomous agency, where autonomy as a form of organization is extended into the behaviour of an agent in interaction with its environment (and not its material self-production). In this thesis, we focus on the development of operational models of sensorimotor agency, exploring the construction of a domain of interactions creating a dynamical interface between agent and environment. We present two main contributions to the study of autonomous agency: First, we contribute to the development of a modelling route for testing, comparing and validating hypotheses about neurocognitive autonomy. Through the design and analysis of specific neurodynamical models embedded in robotic agents, we explore how an agent is constituted in a sensorimotor space as an autonomous entity able to adaptively sustain its own organization. Using two simulation models and different dynamical analysis and measurement of complex patterns in their behaviour, we are able to tackle some theoretical obstacles preventing the understanding of sensorimotor autonomy, and to generate new predictions about the nature of autonomous agency in the neurocognitive domain. Second, we explore the extension of sensorimotor forms of autonomy into the social realm. We analyse two cases from an experimental perspective: the constitution of a collective subject in a sensorimotor social interactive task, and the emergence of an autonomous social identity in a large-scale technologically-mediated social system. Through the analysis of coordination mechanisms and emergent complex patterns, we are able to gather experimental evidence indicating that in some cases social autonomy might emerge based on mechanisms of coordinated sensorimotor activity and interaction, constituting forms of collective autonomous agency

Tensegrity and Recurrent Neural Networks: Towards an Ecological Model of Postural Coordination

Tensegrity systems have been proposed as both the medium of haptic perception and the functional architecture of motor coordination in animals. However, a full working model integrating those two aspects with some form of neural implementation is still lacking. A basic two-dimensional cross-tensegrity plant is designed and its mechanics simulated. The plant is coupled to a Recurrent Neural Network (RNN). The model’s task is to maintain postural balance against gravity despite the intrinsically unstable configuration of the plant. The RNN takes only proprioceptive input about the springs’ lengths and rate of length change and outputs minimum lengths for each spring which modulates their interaction with the plant’s inertial kinetics. Four artificial agents are evolved to coordinate the patterns of spring contractions in order to maintain dynamic equilibrium. A first study assesses quiet standing performance and reveals coordinative patterns between the tensegrity rods akin to humans’ strategy of anti-phase hip-ankle relative phase. The agents show a mixture of periodic and aperiodic trajectories of their Center of Mass. Moreover, the agents seem to tune to the anticipatory “time-to-balance” quantity in order to maintain their movements within a region of reversibility. A second study perturbs the systems with mechanical platform shifts and sensorimotor degradation. The agents’ response to the mechanical perturbation is robust. Dimensionality analysis of the RNNs’ unit activations reveals a pattern of degree of freedom recruitment after perturbation. In the degradation sub-study, different levels of noise are added to the RNN inputs and different levels of weakening gain are applied to the forces generated by the springs to mimic haptic degradation and muscular weakening in elderly humans. As expected, the systems perform less well, falling earlier than without the insults. However, the same systems re-evolved again under the degraded conditions see significant functional recovery. Overall, the dissertation supports the plausibility of RNN cum tensegrity models of haptics-guided postural coordination in humans

Getting their acts together: A coordinated systems approach to extended cognition

A cognitive system is a set of processes responsible for intelligent behaviour. This thesis is an attempt to answer the question: how can cognitive systems be demarcated; that is, what criterion can be used to decide where to draw the boundary of the system? This question is important because it is one way of couching the hypothesis of extended cognition – is it possible for cognitive systems to transcend the boundary of the brain or body of an organism? Such a criterion can be supplied by what is called in the literature a ‘mark of the cognitive’. The main task of this thesis is to develop a general mark of the cognitive. The starting point is that a system responsible for intelligent behaviour is a coordinated coalition of processes. This account proposes a set of functional conditions for coordination. These conditions can then be used as a sufficient condition for membership of a cognitive system. In certain circumstances, they assert that a given process plays a coordination role in the system and is therefore part of the system. The controversy in the extended cognition debate surrounds positive claims of systemhood concerning ‘external’ processes so a sufficient condition will help settle some of these debates. I argue that a Coordinated Systems Approach like this will help to move the extended cognition debate forward from its current impasse. Moreover, the application of the approach to social systems and stygmergic systems - systems where current processes are coordinated partly by the trace of previous action – promises new directions for research

The application of human motor control principles to a collective robotic arm

Current robots are no match for biological organisms when adapting to real-world, dynamic environments. Collective control strategies, such as those used by synergistic biological systems composed of large numbers of identical parts like the human nervous system, provide a novel and alternative approach for the design of fault-tolerant, adaptable robotic systems that have traditionally relied on centralized control. In this research, a robotic arm composed of multiple identical segments in a collective computational architecture was tested for its ability to produce adaptive pointing and reaching behavior. The movement rules for these robotic arm segments were based on the concepts of the "reflex arc" and the "action system" in the human nervous system. Robotic arms of three to seven encapsulated segments were tested. These arms received no central directions and used no direct informational exchange. The arms were sensor-driven at their distal, or leading, outstretched ends to maximize pointing accuracy on a two-dimensional target plane. The remaining non-distal segments in the arms were moved in a sequential order using sensed locally-available movement information about neighboring segments. Successful pointing and reaching behavior was observed in situations with and without movement obstacles. This led to the conclusion that because such behavior was not specified within each segment, the overall arm behavior emerged due to the interaction and coordination of all segments, rather than due to any single segment, centrally-controlled influence, or explicit inter-segmental method of communication