Luis A. Fuente

Artificial Biochemical Networks: Evolving Dynamical Systems to Control Dynamical Systems

Biological organisms exist within environments in which complex nonlinear dynamics are ubiquitous. They are coupled to these environments via their own complex dynamical networks of enzyme-mediated reactions, known as biochemical networks. These networks, in turn, control the growth and behavior of an organism within its environment. In this paper, we consider computational models whose structure and function are motivated by the organization of biochemical networks. We refer to these as artificial biochemical networks and show how they can evolve to control trajectories within three behaviorally diverse complex dynamical systems: 1) the Lorenz system; 2) Chirikovs standard map; and 3) legged robot locomotion. More generally, we consider the notion of evolving dynamical systems to control dynamical systems, and discuss the advantages and disadvantages of using higher order coupling and configurable dynamical modules (in the form of discrete maps) within artificial biochemical networks (ABNs). We find both approaches to be advantageous in certain situations, though we note that the relative tradeoffs between different models of ABN strongly depend on the type of dynamical systems being controlled.

The incorporation of epigenetics in artificial gene regulatory networks.

Artificial gene regulatory networks are computational models that draw inspiration from biological networks of gene regulation. Since their inception they have been used to infer knowledge about gene regulation and as methods of computation. These computational models have been shown to possess properties typically found in the biological world, such as robustness and self organisation. Recently, it has become apparent that epigenetic mechanisms play an important role in gene regulation. This paper describes a new model, the Artificial Epigenetic Regulatory Network (AERN) which builds upon existing models by adding an epigenetic control layer. Our results demonstrate that AERNs are more adept at controlling multiple opposing trajectories when applied to a chaos control task within a conservative dynamical system, suggesting that AERNs are an interesting area for further investigation.

Computational models of signalling networks for non-linear control

Artificial signalling networks (ASNs) are a computational approach inspired by the signalling processes inside cells that decode outside environmental information. Using evolutionary algorithms to induce complex behaviours, we show how chaotic dynamics in a conservative dynamical system can be controlled. Such dynamics are of particular interest as they mimic the inherent complexity of non-linear physical systems in the real world. Considering the main biological interpretations of cellular signalling, in which complex behaviours and robust cellular responses emerge from the interaction of multiple pathways, we introduce two ASN representations: a stand-alone ASN and a coupled ASN. In particular we note how sophisticated cellular communication mechanisms can lead to effective controllers, where complicated problems can be divided into smaller and independent tasks.

Biochemical connectionism

In this paper, we discuss computational architectures that are motivated by connectionist patterns that occur in biochemical networks, and speculate about how this biochemical approach to connectionism might complement conventional neural approaches. In particular, we focus on three features of biochemical networks that make them distinct from neural networks: their diverse, complex nodal processes, their emergent organisation, and the dynamical behaviours that result from higher-order, self-modifying processes. We also consider the growing use of evolutionary algorithms in the design of connectionist systems, noting how this enables us to explore a wider range of connectionist architectures, and how the close relationship between biochemical networks and biological evolution can guide us in this endeavour.

The artificial epigenetic network

In this paper we describe an Artificial Gene Regulatory Network (AGRN), whose form and function are inspired by biological epigenetics. This new architecture, termed an Artificial Epigenetic Network (AEN), is applied to the coupled inverted pendulum task, a control task that has complex non-linear dynamics. The AENs show significant benefits over previous AGRNs. Firstly, when applied to the coupled inverted pendulum task, they show a significant performance increase. In addition, the AENs self-partition, applying different genes to control different dynamics within the task, which is more analogous to gene regulation in nature. These networks also make it possible to gain user control over the dynamics of the network via the modification of the epigenetic layer.

Using artificial epigenetic regulatory networks to control complex tasks within chaotic systems

Artificial gene regulatory networks are computational models which draw inspiration from real world networks of biological gene regulation. Since their inception they have been used to infer knowledge about gene regulation and as methods of computation. These computational models have been shown to possess properties typically found in the biological world such as robustness and self organisation. Recently, it has become apparent that epigenetic mechanisms play an important role in gene regulation. This paper introduces a new model, the Artificial Epigenetic Regulatory Network (AERN) which builds upon existing models by adding an epigenetic control layer. The results demonstrate that the AERNs are more adept at controlling multiple opposing trajectories within Chirikovs standard map, suggesting that AERNs are an interesting area for further investigation.

Adaptive robotic gait control using coupled artificial signalling networks, hopf oscillators and inverse kinematics

A novel bio-inspired architecture comprising three layers is introduced for a six-legged robot in order to generate adaptive rhythmic locomotion patterns using environmental information. Taking inspiration from the intracellular signalling processes that decode environmental information, and considering the emergent behaviours that arise from the interaction of multiple signalling pathways, we develop a decentralised robot controller composed of a collection of artificial signalling networks. Crosstalk, a biological signalling mechanism, is used to couple such networks favouring their interaction. We also apply nonlinear oscillators to model gait generators, which induce symmetric and rhythmical locomotion movements. The trajectories are modulated by a coupled artificial signalling network, which yields adaptive and stable robotic locomotive patterns. Gait trajectories are converted into joint angles by means of inverse kinematics. The architecture is implemented in a simulated version of the real robot T-Hex. Our results demonstrate the ability of the architecture to generate adaptive and periodic gaits.

Influence of Upper Body Pose Mirroring in Human-Robot Interaction

This paper explores the effect of upper body pose mirroring in human-robot interaction. A group of participants is used to evaluate how imitation by a robot affects people’s perception of their conversation with it. A set of twelve questions about the participants’ university experience serves as a backbone for the dialogue structure. In our experimental evaluation, the robot reacts in one of three ways to the human upper body pose: ignoring it, displaying its own upper body pose, and mirroring it. The manner in which the robot behaviour influences human appraisal is analysed using the standard Godspeed questionnaire. Our results show that robot body mirroring/non-mirroring influences the perceived humanness of the robot. The results also indicate that body pose mirroring is an important factor in facilitating rapport and empathy in human social interactions with robots.

Leader-Follower Strategies for Robot-Human Collaboration

This paper considers the impact that robot collaboration strategies have on their human collaborators. In particular, we are interested in how robot leader/follower strategies affect perceived safety and perceived intelligence, which, we argue, are essential for establishing trust and enabling true collaboration between human and robot. We propose an experiment which will enable us to evaluate the impact of leader/follower collaboration strategies on perceived safety and intelligence.

Design of a biologically inspired humanoid neck

This paper presents the design of a novel anthropomorphic robotic neck. It mimics the range of movements found in the human neck, actuated by pneumatic artificial muscles. The proposed humanoid neck simulates the anatomical functionality and structure of a human neck. Specifications are made according to biological, anatomical and behavioural data. The preliminary results show that the proposed humanoid neck is able to deliver the range of movements and head velocities comparable to those observed in human necks. These results also demonstrate that biologically inspired musculoskeletal robotic systems represent a reliable and robust platform to investigate motion development.