Thesis (Ph. D.)--State University of New York at Binghamton, Mechanical Engineering Dept., 2005. / Includes bibliographical references.

Thesis (M.S.)--University of Wisconsin--Madison, 1967. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

Thesis (M.S.)--State University of New York at Binghamton, Dept. of Mechanical Engineering, 2007. / Includes bibliographical references.

Thesis (Ph. D.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Mechanical Engineering, 2008. / Includes bibliographical references.

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.

The temporal structure of neural oscillations has become a widespread hypothetical \mechanism" to explain how neurodynamics give rise to neural functions. Despite the great number of empirical experiments in neuroscience and mathematical and computa- tional modelling investigating the temporal structure of the oscillations, there are still few systematic studies proposing dynamical explanations of how it operates within closed sensorimotor loops of agents performing minimally cognitive behaviours. In this thesis we explore this problem by developing and analysing theoretical models of evolutionary robotics controlled by oscillatory networks. The results obtained suggest that: i) the in- formational content in an oscillatory network about the sensorimotor dynamics is equally distributed throughout the entire range of phase relations; neither synchronous nor desyn- chronous oscillations carries a privileged status in terms of informational content in relation to an agent's sensorimotor activity; ii) although the phase relations of oscillations with a narrow frequency difference carry a relatively higher causal relevance than the rest of the phase relations to sensorimotor coordinations, overall there is no privileged functional causal contribution to either synchronous or desynchronous oscillations; and iii) oscilla- tory regimes underlying functional behaviours (e.g. phototaxis, categorical perception) are generated and sustained by the agent's sensorimotor loop dynamics, they depend not only on the dynamic structure of a sensory input but also on the coordinated coupling of the agent's motor-sensory dynamics. This thesis also contributes to the Coordination Dynam- ics framework (Kelso, 1995) by analysing the dynamics of the HKB (Haken-Kelso-Bunz) equation within a closed sensorimotor loop and by discussing the theoretical implications of such an analysis. Besides, it contributes to the ongoing philosophical debate about whether actions are either causally relevant or a constituent of cognitive functionalities by bringing this debate to the context of oscillatory neurodynamics and by illustrating the constitutive notion of actions to cognition.

Currently the central challenge facing evolutionary robotics is to determine how best to extend the range and complexity of behaviour supported by evolved neural systems. Implicit in the work described in this thesis is the idea that this might best be achieved through devising neural circuits (tractable to evolutionary exploration) that exhibit complementary functional characteristics. We concentrate on two problem domains; locomotion and sequence learning. For locomotion we compare the use of GasNets and other adaptive networks. For sequence learning we introduce a novel connectionist model inspired by the role of dopamine in the basal ganglia (commonly interpreted as a form of reinforcement learning). This connectionist approach relies upon a new neuron model inspired by notions of energy efficient signalling. Two reward adaptive circuit variants were investigated. These were applied respectively to two learning problems; where action sequences are required to take place in a strict order, and secondly, where action sequences are robust to intermediate arbitrary states. We conclude the thesis by proposing a formal model of functional integration, encompassing locomotion and sequence learning, extending ideas proposed by W. Ross Ashby. A general model of the adaptive replicator is presented, incoporating subsystems that are tuned to continuous variation and discrete or conditional events. Comparisons are made with Ross W. Ashby's model of ultrastability and his ideas on adaptive behaviour. This model is intended to support our assertion that, GasNets (and similar networks) and reward adaptive circuits of the type presented here, are intrinsically complementary. In conclusion we present some ideas on how the co-evolution of GasNet and reward adaptive circuits might lead us to significant improvements in the synthesis of agents capable of exhibiting complex adaptive behaviour.

Thesis (PhD) -- University of South Australia, 1998

Thesis (Ph. D.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Mechanical Engineering, 2009. / Includes bibliographical references.

The ability to generate and control assembling structures built over many orders of magnitude is an unsolved challenge of engineering and science. Many of the presumed transformational benefits of nanotechnology and robotics are based directly on this capability. There are still significant theoretical difficulties associated with building such systems, though technology is rapidly ensuring that the tools needed are becoming available in chemical, electronic, and robotic domains. In this thesis a simulated, general-purpose computational prototype is developed which is capable of unlimited assembly and controlled by external input, as well as an additional prototype which, in structures, can emulate any other computing device. These devices are entirely finite-state and distributed in operation. Because of these properties and the unique ability to form unlimited size structures of unlimited computational power, the prototypes represent a novel and useful blueprint on which to base scalable assembly in other domains. A new assembling model of Computational Organization and Regulation over Assembly Levels (CORAL) is also introduced, providing the necessary framework for this investigation. The strict constraints of the CORAL model allow only an assembling unit of a single type, distributed control, and ensure that units cannot be reprogrammed - all reprogramming is done via assembly. Multiple units are instead structured into aggregate computational devices using a procedural or developmental approach. Well-defined comparison of computational power between levels of organization is ensured by the structure of the model. By eliminating ambiguity, the CORAL model provides a pragmatic answer to open questions regarding a framework for hierarchical organization. Finally, a comparison between the designed prototypes and units evolved using evolutionary algorithms is presented as a platform for further research into novel scalable assembly. Evolved units are capable of recursive pairing ability under the control of a signal, a primitive form of unlimited assembly, and do so via symmetry-breaking operations at each step. Heuristic evidence for a required minimal threshold of complexity is provided by the results, and challenges and limitations of the approach are identified for future evolutionary studies.