A size-weight illusion (SWI) occurs when a large object and small object of equal mass but different volume are lifted and the small object is perceived as heavier than the large object. All previous studies of the SWI used similar coloured objects and found that individuals initially use more force to lift the large object, compared to the small object but then use similar forces for the two objects on subsequent lifts. In contrast to the change in lifting forces over trials, the perceptual illusion stays consistent across all trials. The goal of the current study was to determine if introducing different colours for the SWI stimuli could alter participants’ expectations about the masses of the two objects and therefore modify the perceptual SWI. Participants lifted SWI stimuli that were either identical in colour or stimuli of different colour.

Thesis (M.S.)--University of Texas at El Paso, 2008. / Title from title screen. Vita. CD-ROM. Includes bibliographical references. Also available online.

The control of complex robotic platforms is a challenging task, especially in designs with high levels of kinematic redundancy. Novel variable impedance actuators (VIAs) have recently demonstrated that, by allowing the ability to simultaneously modulate the output torque and impedance, one can achieve energetically more efficient and safer behaviour. However, this adds further levels of actuation redundancy, making planning and control of such systems even more complicated. VIAs are designed with the ability to mechanically modulate impedance during movement. Recent work from our group, employing the optimal control (OC) formulation to generate impedance policies, has shown the potential benefit of VIAs in tasks requiring energy storage, natural dynamic exploitation and robustness against perturbation. These approaches were, however, restricted to systems with smooth, continuous dynamics, performing tasks over a predefined time horizon. When considering tasks involving multiple phases of movement, including switching dynamics with discrete state transitions (resulting from interactions with the environment), traditional approaches such as independent phase optimisation would result in a potentially suboptimal behaviour. Our work addresses these issues by extending the OC formulation to a multiphase scenario and incorporating temporal optimisation capabilities (for robotic systems with VIAs). Given a predefined switching sequence, the developed methodology computes the optimal torque and impedance profile, alongside the optimal switching times and total movement duration. The resultant solution minimises the control effort by exploiting the actuation redundancy and modulating the natural dynamics of the system to match those of the desired movement. We use a monopod hopper and a brachiation system in numerical simulations and a hardware implementation of the latter to demonstrate the effectiveness and robustness of our approach on a variety of dynamic tasks. The performance of model-based control relies on the accuracy of the dynamics model. This can deteriorate significantly due to elements that cannot be fully captured by analytic dynamics functions and/or due to changes in the dynamics. To circumvent these issues, we improve the performance of the developed framework by incorporating an adaptive learning algorithm. This performs continuous data-driven adjustments to the dynamics model while re-planning optimal policies that reflect this adaptation. The results presented show that the augmented approach is able to handle a range of model discrepancies, in both simulation and hardware experiments using the developed robotic brachiation system.

The concept of self-directed learning (SDL) has been extensively studied; however, the majority of studies have explored learners’ perspectives on SDL, with less attention paid to investigating SDL from educators’ perspectives. Surprisingly, while assessment and feedback have long been recognized as powerful elements which influence how learners approach their learning, and key research studies have examined how both assessment and feedback can encourage and enhance the development of SDL, this nevertheless remains an area that would benefit from increased attention. Moreover, although there is a growing body of literature investigating the cultural dimension of SDL, most of these studies are limited to examining the formation of SDL among individuals influenced by Western or Confucian cultures, ignoring the existence of other cultural groups. This study, which investigates Malaysian teacher educators’ conceptualisations of SDL, begins to address these gaps. The key research questions which guided the study are: 1) How do teacher educators in Malaysia conceptualise learning? 2) How do teacher educators in Malaysia conceptualise SDL? 3) To what extent do teacher educators in Malaysia perceive themselves as self-directed learners? 4) What kind of learning opportunities do teacher educators in Malaysia create for their learners to foster the development of SDL, and what is the particular role of assessment and feedback in SDL? Twenty Malaysian teacher educators were interviewed to obtain their views on SDL and to identify their pedagogical practices which may foster or hinder the development of SDL approaches among their learners. A constructivist grounded theory approach was used to inform the methodological framework of this study, whilst a hybrid inductive and deductive analysis approach was used to analyse the interview data. The findings of the current study suggest that most assessment and feedback practices are heavily focused on assessments designed by educators and on educator-generated feedback, in which learners are passive recipients. It is argued that these practices have significantly contradicted the primary principle of SDL, which characterises the learner as the key agent of his or her own learning. The findings of this study suggest that a more comprehensive conceptualisation of SDL is required that recognises the fundamental role of both the self and of educators in SDL, and acknowledges the impact of the socio-cultural context on SDL. Informed by the existing SDL literature, and derived from fine-grained analysis of the interview data, the proposed definition of SDL and reconceptualised SDL framework foreground SDL as socially constructed learning where the learner takes control of his or her own learning processes within complex socio cultural contexts. The thesis concludes by recommending that future research (i) explores the central role of assessment and feedback in the context of SDL and (ii) investigates the impact of various cultures on learning, in order to develop a broader and more nuanced understanding of SDL.

While stochastic optimal control, together with associate formulations like Reinforcement Learning, provides a formal approach to, amongst other, motor control, it remains computationally challenging for most practical problems. This thesis is concerned with the study of relations between stochastic optimal control and probabilistic inference. Such dualities { exempli ed by the classical Kalman Duality between the Linear-Quadratic-Gaussian control problem and the filtering problem in Linear-Gaussian dynamical systems { make it possible to exploit advances made within the separate fields. In this context, the emphasis in this work lies with utilisation of approximate inference methods for the control problem. Rather then concentrating on special cases which yield analytical inference problems, we propose a novel interpretation of stochastic optimal control in the general case in terms of minimisation of certain Kullback-Leibler divergences. Although these minimisations remain analytically intractable, we show that natural relaxations of the exact dual lead to new practical approaches. We introduce two particular general iterative methods ψ-Learning, which has global convergence guarantees and provides a unifying perspective on several previously proposed algorithms, and Posterior Policy Iteration, which allows direct application of inference methods. From these, practical algorithms for Reinforcement Learning, based on a Monte Carlo approximation to ψ-Learning, and model based stochastic optimal control, using a variational approximation of posterior policy iteration, are derived. In order to overcome the inherent limitations of parametric variational approximations, we furthermore introduce a new approach for none parametric approximate stochastic optimal control based on a reproducing kernel Hilbert space embedding of the control problem. Finally, we address the general problem of temporal optimisation, i.e., joint optimisation of controls and temporal aspects, e.g., duration, of the task. Specifically, we introduce a formulation of temporal optimisation based on a generalised form of the finite horizon problem. Importantly, we show that the generalised problem has a dual finite horizon problem of the standard form, thus bringing temporal optimisation within the reach of most commonly used algorithms. Throughout, problems from the area of motor control of robotic systems are used to evaluate the proposed methods and demonstrate their practical utility.

Thesis (M.S.)--Ohio University, November, 1993. / Title from PDF t.p.

<div>The recent progress of machine learning, driven by pervasive data and increasing computational power, has shown its potential to achieve higher robot autonomy. Yet, with too much focus on generic models and data-driven paradigms while ignoring inherent structures of control systems and tasks, existing machine learning methods typically suffer from data and computation inefficiency, hindering their public deployment onto general real-world robots. In this thesis work, we claim that the efficiency of autonomous robot learning can be boosted by two strategies. One is to incorporate the structures of optimal control theory into control-objective learning, and this leads to a series of control-induced learning methods that enjoy the complementary benefits of machine learning for higher algorithm autonomy and control theory for higher algorithm efficiency. The other is to integrate necessary human guidance into task and control objective learning, leading to a series of paradigms for robot learning with minimal human guidance on the loop.</div><div><br></div><div>The first part of this thesis focuses on the control-induced learning, where we have made two contributions. One is a set of new methods for inverse optimal control, which address three existing challenges in control objective learning: learning from minimal data, learning time-varying objective functions, and learning under distributed settings. The second is a Pontryagin Differentiable Programming methodology, which bridges the concepts of optimal control theory, deep learning, and backpropagation, and provides a unified end-to-end learning framework to solve a broad range of learning and control tasks, including inverse reinforcement learning, neural ODEs, system identification, model-based reinforcement learning, and motion planning, with data- and computation- efficient performance.</div><div><br></div><div>The second part of this thesis focuses on the paradigms for robot learning with necessary human guidance on the loop. We have made two contributions. The first is an approach of learning from sparse demonstrations, which allows a robot to learn its control objective function only from human-specified sparse waypoints given in the observation (task) space; and the second is an approach of learning from</div><div>human’s directional corrections, which enables a robot to incrementally learn its control objective, with guaranteed learning convergence, from human’s directional correction feedback while it is acting.</div><div><br></div>

No description available.

Thesis (M.Sc. [i.e. M.C.M.S.])--University of Waikato, 2006. / Includes bibliographical references (p. 167-173) Also available via the World Wide Web.

The purpose of the study is to explore relationships among elementary school students&amp / #8217 / gender, relevant prior knowledge, meaningful learning orientation, reasoning ability, self-efficacy, locus of control, attitudes toward science and achievement in genetics in learning cycle and traditional classrooms. The study was conducted on 213 8th grade students from eight classes of two public elementary schools in Ankara in 2005-2006 Spring-semester. Students in the experimental group (N=104) received learning cycle instruction that helps students acquire conceptual understanding of scientific concepts, and the students in the control group (N=109) received traditional instruction. The students were given Genetics Achievement Test as a pre-test before and as a post-test after the instruction. Students were also given Learning Approach Questionnaire that measures students&amp / #8217 / learning orientations and Test of Logical Thinking that determines students&amp / #8217 / reasoning abilities. Students&amp / #8217 / levels of self-efficacy, locus of control and their attitudes toward science also were measured. One-way ANOVA analysis revealed that learning cycle instruction improved students&amp / #8217 / achievement in genetics compared to traditional instruction. Stepwise multiple regression analysis revealed that in learning cycle classrooms, the main predictors of achievement in genetics were students&amp / #8217 / meaningful learning orientation (49.6%) and their attitudes toward science (11.8%). In traditional classrooms, students&amp / #8217 / attitudes toward science (44%) and reasoning ability (9.8%) were the main predictors of achievement while remaining 5.7% of the variance explained by relevant prior knowledge, locus of control and meaningful learning orientation. This study revealed that different variables may be important for 8th grade students&amp / #8217 / genetics achievement in learning cycle and traditional classes.