Bio­-inspired approaches to the control and modelling of an anthropomimetic robot

Introducing robots into human environments requires them to handle settings designed specifically for human size and morphology, however, large, conventional humanoid robots with stiff, high powered joint actuators pose a significant danger to humans. By contrast, “anthropomimetic” robots mimic both human morphology and internal structure; skeleton, muscles, compliance and high redundancy. Although far safer, their resultant compliant structure presents a formidable challenge to conventional control. Here we review, and seek to address, characteristic control issues of this class of robot, whilst exploiting their biomimetic nature by drawing upon biological motor control research. We derive a novel learning controller for discovering effective reaching actions created through sustained activation of one or more muscle synergies, an approach which draws upon strong, recent evidence from animal and humans studies, but is almost unexplored to date in musculoskeletal robot literature. Since the best synergies for a given robot will be unknown, we derive a deliberately simple reinforcement learning approach intended to allow their emergence, in particular those patterns which aid linearization of control. We also draw upon optimal control theories to encourage the emergence of smoother movement by incorporating signal dependent noise and trial repetition. In addition, we argue the utility of developing a detailed dynamic model of a complete robot and present a stable, physics-­‐‑based model, of the anthropomimetic ECCERobot, running in real time with 55 muscles and 88 degrees of freedom. Using the model, we find that effective reaching actions can be learned which employ only two sequential motor co-­‐‑activation patterns, each controlled by just a single common driving signal. Factor analysis shows the emergent muscle co-­‐‑activations can be reconstructed to significant accuracy using weighted combinations of only 13 common fragments, labelled “candidate synergies”. Using these synergies as drivable units the same controller learns the same task both faster and better, however, other reaching tasks perform less well, proportional to dissimilarity; we therefore propose that modifications enabling emergence of a more generic set of synergies are required. Finally, we propose a continuous controller for the robot, based on model predictive control, incorporating our model as a predictive component for state estimation, delay-­‐‑ compensation and planning, including merging of the robot and sensed environment into a single model. We test the delay compensation mechanism by controlling a second copy of the model acting as a proxy for the real robot, finding that performance is significantly improved if a precise degree of compensation is applied and show how rapidly an un-­‐‑compensated controller fails as the model accuracy degrades

Application of Bayesian networks to problems within obesity epidemiology

Obesity is a significant public health problem in the United Kingdom and many other parts of the world, including some low-income settings. Although obesity prevalence has been rising for several decades, governments have been slow to implement policies that may have an impact at a population level. Numerous socio-demographic factors have been linked with obesity, but are highly intercorrelated, and identifying relevant factors or at-risk population groups is difficult. This thesis uses a graphical modelling approach, specifically Bayesian networks, to model the joint distribution of socio-demographic factors and obesity related behaviour. The key advantages of graphical models in this context are their ability to model highly correlated data, and to represent complex relationships efficiently as network structure. Three separate pieces of work comprise this thesis. The first uses a sampling technique to identify the networks that best explain the observed data, and employs the common structural features of these networks to infer conditional dependencies present between socio-demographic variables and obesity related behaviour indicators. We find determinants of recreational physical activity differ between males and females, and age and ethnicity have a significant influence on snacking behaviour. The second piece of work usesBayesian networks to build a model of health behaviour given socio demographic input, and then applies this to data from the 2001 census in order to provide an estimate of the health behaviour of a real population. The final analysis uses Bayesian network structure to explore potential determinants of body fat deposition patterns and compares the results tothose derived from a Generalized Linear Model (GLM). Our approach successfully identifies the main determinants, age and Body Mass Index, although is not a genuine alternative due to a lack of sensitivity to less important determinants. Beyond the application to obesity, results of this thesis are of a wider relevance to epidemiology as the field moves towards an increased use of Machine Learning techniques. The work conducted has also met and overcome several technical issues that are likely to be of relevance to others exploring similar approaches.EThOS - Electronic Theses Online ServiceGBUnited Kingdo