The power of affective touch within social robotics

There have been many leaps and bounds within social robotics, especially within human-robot interaction and how to make it a more meaningful relationship. This is traditionally accomplished through communicating via vision and sound. It has been shown that humans naturally seek interaction through touch yet the implications on emotions is unknown both in human-human interaction and social human-robot interaction. This thesis unpacks the social robotics community and the research undertaken to show a significant gap in the use of touch as a form of communication. The meaning behind touch will be investigated and what implication it has on emotions. A simplistic prototype was developed focusing on texture and breathing. This was used to carry out experiments to find out which combination of texture and movement felt natural. This proved to be a combination of synthetic fur and 14 breaths per minute. For human’s touch is said to be the most natural way of communicating emotions, this is the first step in achieving successful human-robot interaction in a more natural human-like way

Robots capaces de aprender y adaptarse al entorno a partir de sus propias experiencias

Entre los objetivos reconocidos en la robótica actual destaca la necesidad de disponer de robots adaptables, capaces de aprender del usuario y de la propia experiencia. Esta adaptación se debe extender a todo el tiempo de vida del robot, los errores y aciertos del robot deben permitir que éste pueda modificar su comportamiento futuro. En este sentido, el paradigma de aprendizaje por refuerzo resulta muy prometedor en la medida en que permite que un robot aprenda sin más información que un refuerzo extrínseco que indica cuando las acciones realizadas son correctas o no. Los algoritmos tradicionales de aprendizaje por refuerzo se limitan a comportamientos reactivos simples y rara vez se aplican al aprendizaje directo en robots moviéndose en entornos reales. De hecho, el aprendizaje por refuerzo suele ser lento y requerir un proceso de exploración costoso. Por otra parte, el tiempo de aprendizaje se incrementa de forma exponencial con el número de estados (situaciones significativamente diferentes) que puede encontrar el robot. Con el objetivo de superar estas limitaciones en esta tesis se abordaron cuatro grandes objetivos: a) Algoritmos más interpretables y con menos parámetros: los algoritmos clásicos de aprendizaje por refuerzo intentan predecir el refuerzo futuro que el robot va a recibir. Esta información es difícilmente interpretable, lo que hace difícil corroborar si el proceso de aprendizaje se está llevando a cabo de forma correcta. Se ha desarrollado un nuevo algoritmo, llamado I_Tbf, capaz de aprender a predecir “cuándo el robot va a cometer un fallo”. La discrepancia entre lo que el sistema predice y lo que realmente sucede nos permite detectar problemas y corregirlos durante el propio proceso de aprendizaje. Las ventajas obtenidas con este algoritmo son: buenos tiempos de aprendizaje, un reducido número de parámetros y mayor interpretabilidad del proceso de aprendizaje. b) Aprendizaje simultáneo de percepción y acción: hemos creado un sistema capaz de aprender al mismo tiempo el espacio de estados y la acción a ejecutar en cada uno de estos estados. Partiendo de nuestro algoritmo I_Tbf, el sistema itera la política de control tratando de maximizar el tiempo a fallo. El espacio de estados se crea de forma dinámica: partiendo de un conjunto vacío se añaden nuevos estados a medida que el robot encuentra nuevas situaciones que no ha visto antes. La creación dinámica del espacio de estados evita el proceso de creación y evaluación de representaciones de estados ad hoc. Para lograr la generación dinámica de estados hemos recurrido a la Teoría de Resonancia Adaptativa (ART) adaptándola a nuestro problema. c) Reducción del tiempo de aprendizaje a través de la creación de comités de aprendedores: para acelerar los procesos de aprendizaje resulta conveniente recurrir a estrategias habituales en el campo de las redes neuronales artificiales dirigidas a evitar el “sobre-aprendizaje” y la falta de generalización. Por este motivo, el uso de comités de “aprendedores” que, mediante diferentes estrategias de voto ponderado, son capaces de seleccionar la acción que debe ejecutar el robot en cada instante, permiten acelerar el proceso de aprendizaje mientras se mantiene una buena generalización. Gracias al incremento de estabilidad proporcionado por el comité, se puede introducir el concepto de aprendizaje continuo, donde el sistema es capaz de aprender durante todo el ciclo de vida del robot, sin que el comportamiento sufra grandes inestabilidades. Se han realizado pruebas donde la señal de refuerzo era proporcionada por un usuario humano. Pese a que dicha señal tenía una gran componente no determinista, gracias a la estabilidad proporcionada por el comité de aprendedores el sistema es capaz de alcanzar la convergencia en pocos minutos. d) Determinación de la relevancia sensorial: muchas de las entradas sensoriales proporcionadas por los modernos sensores de alta resolución son irrelevantes para la tarea que el robot está intentando aprender. Estas dimensiones irrelevantes pueden provocar errores en las estrategias de clustering. Esto es algo conocido como la maldición de las dimensiones. En esta tesis se ha investigado el uso de criterios estadísticos basados en la teoría de la información, y la información mutua, para determinar, de forma dinámica, el subconjunto de sensores que es realmente relevante para lo que el robot quiere aprender. El uso de este subconjunto de sensores proporciona una reducción significativa del tiempo de aprendizaje, así como un aumento de la robustez de los comportamientos

Hybrid conditional planning for service robotics

Planning is an indispensable ability for intelligent service robots operating in unstructured environments. Given service robots commonly have incomplete knowledge about and partial observability of handle such uncertainty. Moreover, the plans they compute should be feasible for real-world execution. Conditional planning is concerned with reaching goals from an initial state, in the presence of incomplete knowledge and partial observability; by utilizing sensing actions. Since all contingencies are considered in advance, a conditional plan is essentially a tree of actions where the root represents the initial state, leaves represent goal states, and each branch of the tree from the root to a leaf represents a possible execution of (deterministic) actuation actions and (non-deterministic) sensing actions to reach a goal state. Hybrid conditional planning extends conditional planning further by integrating lowlevel feasibility checks into executability conditions of actuation actions in conditional plans. We introduce a parallel offline algorithm called HCPlan, for computing hybrid conditional plans in robotics applications. HCPlan relies on modeling actuation actions and sensing actions in the causality-based action description language C+, and computation of the branches of a conditional plan in parallel using a SAT solver. In particular, thanks to external atoms, continuous feasibility checks (such as collision and reachability checks) are embedded into causal laws representing actuation actions and sensing actions; and thus each branch of a hybrid conditional plan describes a feasible execution of actions to reach their goals. Utilizing causal laws that describe iv non-deterministic effects of actions, sensing actions can be explicitly formalized; and thus each branch of a conditional plan can be computed without necessitating an ordering of sensing actions in advance. Furthermore, we introduce two different extensions of our hybrid conditional planner HCPlan: HCPlan-Anytime and HCPlan-Reactive. HCPlan-Anytime computes a partial hybrid conditional plan within a given time, by generating the branches with respect to their probability of execution. HCPlan-Reactive computes a hybrid conditional plan with a receding horizon. These extensions trade-off completeness of hybrid conditional plans for improved computation time, and provide useful important variations towards real-time use of the hybrid conditional planning. We develop comprehensive benchmarks for service robotics domain and evaluate our approach over these benchmarks with extensive experiments in terms of computational efficiency and plan quality. We compare HCPlan with other related conditional planners and approaches. We further demonstrate the usefulness of our approach in service robotics applications through dynamic simulations and physical implementations

AN INVESTIGATION OF ELECTROMYOGRAPHIC (EMG) CONTROL OF DEXTROUS HAND PROSTHESES FOR TRANSRADIAL AMPUTEES

In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of Plymouth University's products or services.There are many amputees around the world who have lost a limb through conflict, disease or an accident. Upper-limb prostheses controlled using surface Electromyography (sEMG) offer a solution to help the amputees; however, their functionality is limited by the small number of movements they can perform and their slow reaction times. Pattern recognition (PR)-based EMG control has been proposed to improve the functional performance of prostheses. It is a very promising approach, offering intuitive control, fast reaction times and the ability to control a large number of degrees of freedom (DOF). However, prostheses controlled with PR systems are not available for everyday use by amputees, because there are many major challenges and practical problems that need to be addressed before clinical implementation is possible. These include lack of individual finger control, an impractically large number of EMG electrodes, and the lack of deployment protocols for EMG electrodes site selection and movement optimisation. Moreover, the inability of PR systems to handle multiple forces is a further practical problem that needs to be addressed. The main aim of this project is to investigate the research challenges mentioned above via non-invasive EMG signal acquisition, and to propose practical solutions to help amputees. In a series of experiments, the PR systems presented here were tested with EMG signals acquired from seven transradial amputees, which is unique to this project. Previous studies have been conducted using non-amputees. In this work, the challenges described are addressed and a new protocol is proposed that delivers a fast clinical deployment of multi-functional upper limb prostheses controlled by PR systems. Controlling finger movement is a step towards the restoration of lost human capabilities, and is psychologically important, as well as physically. A central thread running through this work is the assertion that no two amputees are the same, each suffering different injuries and retaining differing nerve and muscle structures. This work is very much about individualised healthcare, and aims to provide the best possible solution for each affected individual on a case-by-case basis. Therefore, the approach has been to optimise the solution (in terms of function and reliability) for each individual, as opposed to developing a generic solution, where performance is optimised against a test population. This work is unique, in that it contributes to improving the quality of life for each individual amputee by optimising function and reliability. The main four contributions of the thesis are as follows: 1- Individual finger control was achieved with high accuracy for a large number of finger movements, using six optimally placed sEMG channels. This was validated on EMG signals for ten non-amputee and six amputee subjects. Thumb movements were classified successfully with high accuracy for the first time. The outcome of this investigation will help to add more movements to the prosthesis, and reduce hardware and computational complexity. 2- A new subject-specific protocol for sEMG site selection and reliable movement subset optimisation, based on the amputee’s needs, has been proposed and validated on seven amputees. This protocol will help clinicians to perform an efficient and fast deployment of prostheses, by finding the optimal number and locations of EMG channels. It will also find a reliable subset of movements that can be achieved with high performance. 3- The relationship between the force of contraction and the statistics of EMG signals has been investigated, utilising an experimental design where visual feedback from a Myoelectric Control Interface (MCI) helped the participants to produce the correct level of force. Kurtosis values were found to decrease monotonically when the contraction level increased, thus indicating that kurtosis can be used to distinguish different forces of contractions. 4- The real practical problem of the degradation of classification performance as a result of the variation of force levels during daily use of the prosthesis has been investigated, and solved by proposing a training approach and the use of a robust feature extraction method, based on the spectrum. The recommendations of this investigation improve the practical robustness of prostheses controlled with PR systems and progress a step further towards clinical implementation and improving the quality of life of amputees. The project showed that PR systems achieved a reliable performance for a large number of amputees, taking into account real life issues such as individual finger control for high dexterity, the effect of force level variation, and optimisation of the movements and EMG channels for each individual amputee. The findings of this thesis showed that the PR systems need to be appropriately tuned before usage, such as training with multiple forces to help to reduce the effect of force variation, aiming to improve practical robustness, and also finding the optimal EMG channel for each amputee, to improve the PR system’s performance. The outcome of this research enables the implementation of PR systems in real prostheses that can be used by amputees.Ministry of Higher Education and Scientific Research and Baghdad University- Baghdad/Ira