Tracking implicit trajectories

Output tracking of implcitly defined reference trajectories is examined. A continuous-time nonlinear dynamical system is constructed that produces explicit estimates of time-varying implicit trajectories. We prove that incorporation of this "dynamic inverter" into a tracking controller provides exponential output tracking of the implicitly defined trajectory for nonlinear control systems having vector relative degree and well-behaved internal dynanmics

Development of a robotic prototype system for the preparation and partition of radioactive products

The ionizing radiation is used in the nuclear medicine field during the execution of diagnosis exams. The continuous exposure of humans to the radiation may cause organs and tissues damage, being its severity dependent of the quantity of the radiation and the exposure time. The main objective of this work is to design a virtual environment to carry out the simulation of the several stages for the preparation of radioactive products based on the use of a robotic arm. In this work, the V-REP robotic simulation tool was used for the specification and development of the manipulation processes, without the need to consider the real manipulator, being timely and costly efficient. During this study, the preparation process of the dosages in the diagnostic exams was analyzed, being posteriorly translated into mechanical processes for a better perception. The materials and equipment needed were designed as virtual 3D models and posteriorly imported into the V-REP simulation platform in order to be distributed and programmed to achieve a closer approximation to reality.info:eu-repo/semantics/publishedVersio

Movement kinematics and proprioception in post-stroke spasticity: assessment using the Kinarm robotic exoskeleton

Background Motor impairment after stroke interferes with performance of everyday activities. Upper limb spasticity may further disrupt the movement patterns that enable optimal function; however, the specific features of these altered movement patterns, which differentiate individuals with and without spasticity, have not been fully identified. This study aimed to characterize the kinematic and proprioceptive deficits of individuals with upper limb spasticity after stroke using the Kinarm robotic exoskeleton. Methods Upper limb function was characterized using two tasks: Visually Guided Reaching, in which participants moved the limb from a central target to 1 of 4 or 1 of 8 outer targets when cued (measuring reaching function) and Arm Position Matching, in which participants moved the less-affected arm to mirror match the position of the affected arm (measuring proprioception), which was passively moved to 1 of 4 or 1 of 9 different positions. Comparisons were made between individuals with (n = 35) and without (n = 35) upper limb post-stroke spasticity. Results Statistically significant differences in affected limb performance between groups were observed in reaching-specific measures characterizing movement time and movement speed, as well as an overall metric for the Visually Guided Reaching task. While both groups demonstrated deficits in proprioception compared to normative values, no differences were observed between groups. Modified Ashworth Scale score was significantly correlated with these same measures. Conclusions The findings indicate that individuals with spasticity experience greater deficits in temporal features of movement while reaching, but not in proprioception in comparison to individuals with post-stroke motor impairment without spasticity. Temporal features of movement can be potential targets for rehabilitation in individuals with upper limb spasticity after stroke.York University Librarie

ROBOT-MEDIATED AND CLINICAL SCALES EVALUATION AFTER UPPER LIMB BOTULINUM TOXIN TYPE A INJECTION IN CHILDREN WITH HEMIPLEGIA

Objective: The aim of this pilot study was to examine changes in different aspects of impairment, including spasticity in the upper limbs, of hemiplegic children following botulinum toxin type A intervention. Progress was assessed using standard clinical measurements and a robotic device. Design: Pre-post multiple baseline. Subjects: Six children with hemiplegia. Methods: Botulinium toxin type A injections were administered into the affected upper limb muscles. Outcomes were evaluated before and one month after the injection. Outcome assessments included: Melbourne Scale, Modified Ashworth Scale (MAS) and Passive Range of Motion. Furthermore, a robotic device was employed as an evaluation tool. Results: Patients treated with botulinum toxin type A had significantly greater reduction in spasticity (MAS, p < 0.01), which explains an improvement in upper limb function and quality movement measured with the Melbourne Scale (p < 0.01). These improvements are consistent with robot-based evaluation results that showed statistically significant changes (p < 0.01) following botulinum toxin type A injections. Conclusion: The upper limb performs a wide variety of movements. The multi-joint nature of the task during the robotmediated evaluation required active control of joint interaction forces. There was good correlation between clinical scales and robotic evaluation. Hence the robot-mediated assessment may be used as an additional tool to quantify the degree of motor improvement after botulinum toxin type A injections

Cognitive Robots for Social Interactions

One of my goals is to work towards developing Cognitive Robots, especially with regard to improving the functionalities that facilitate the interaction with human beings and their surrounding objects. Any cognitive system designated for serving human beings must be capable of processing the social signals and eventually enable efficient prediction and planning of appropriate responses. My main focus during my PhD study is to bridge the gap between the motoric space and the visual space. The discovery of the mirror neurons ([RC04]) shows that the visual perception of human motion (visual space) is directly associated to the motor control of the human body (motor space). This discovery poses a large number of challenges in different fields such as computer vision, robotics and neuroscience. One of the fundamental challenges is the understanding of the mapping between 2D visual space and 3D motoric control, and further developing building blocks (primitives) of human motion in the visual space as well as in the motor space. First, I present my study on the visual-motoric mapping of human actions. This study aims at mapping human actions in 2D videos to 3D skeletal representation. Second, I present an automatic algorithm to decompose motion capture (MoCap) sequences into synergies along with the times at which they are executed (or "activated") for each joint. Third, I proposed to use the Granger Causality as a tool to study the coordinated actions performed by at least two units. Recent scientific studies suggest that the above "action mirroring circuit" might be tuned to action coordination rather than single action mirroring. Fourth, I present the extraction of key poses in visual space. These key poses facilitate the further study of the "action mirroring circuit". I conclude the dissertation by describing the future of cognitive robotics study

Advancing automation and robotics technology for the space station and for the US economy: Submitted to the United States Congress October 1, 1987

In April 1985, as required by Public Law 98-371, the NASA Advanced Technology Advisory Committee (ATAC) reported to Congress the results of its studies on advanced automation and robotics technology for use on the space station. This material was documented in the initial report (NASA Technical Memorandum 87566). A further requirement of the Law was that ATAC follow NASA's progress in this area and report to Congress semiannually. This report is the fifth in a series of progress updates and covers the period between 16 May 1987 and 30 September 1987. NASA has accepted the basic recommendations of ATAC for its space station efforts. ATAC and NASA agree that the mandate of Congress is that an advanced automation and robotics technology be built to support an evolutionary space station program and serve as a highly visible stimulator affecting the long-term U.S. economy

Learning from Demonstration using Hierarchical Inverse Reinforcement Learning

Com a evolução conjunta da Indústria e Robótica, os sistemas produtivos tornaram-se cada vez mais complexos, robustos e seguros. No entanto, a sociedade requer que a Indústria se adapte às suas necessidades de uma forma rápida, suave e flexível. A programação contemporânea incorpora metodologias inteligentes no chão de fábrica. Contudo, estas não são flexíveis ou recicláveis quando expostas a novas configurações de produto ou produção. De igual modo, as metodologias raramente conseguem transferir capacidades a outras tarefas. Tudo isto combinado, consome muitos recursos. O primeiro objetivo foi criar um cenário de simulação no seio da Indústria, que fosse facilmente transferido para situações reais, ao mesmo tempo que seguisse as técnicas de Reinforcement Learning. O segundo objetivo foi o estudo sobre como discretizar uma tarefa. Por fim, o último objetivo envolveu avaliar o impacto da reutilização de modelos pré-treinados em diferentes sub-tarefas. A metodologia escolhida recorre à framework robo-gym para interligar o OpenAI Gym com o simulador de física Gazebo e assim criar o cenário de pick and place modificado. A modificação corresponde à adição da tarefa de ajuste do objeto ao encaixe desejado. O treino do agente envolve demonstrações, tal como definido na metodologia denominada de Learning from Demonstration. O algoritmo de treino escolhido foi o Generative Adversarial Imitation Learning, que partilha características tanto de Reinforcement Learning como de Inverse Reinforcement Learning. A primeira conclusão principal foi a de que discretização de tarefas pode ser realizada através da modulação da função de recompensa. A equação deve considerar um gradiente suave de erro e recompensas positivas associadas à conclusão de cada uma das sub-tarefas. As recompensas positivas devem, por um lado, ser sequencialmente maiores, à medida que a sequência progride. Por outro lado, os incrementos entre as recompensas devem também ser crescentes. Assim, a metodologia de discretização consegue simplificar tarefas ditas complexas e melhora desempenho dos agentes, face às metodologias tradicionais. Adicionalmente, provámos que o re-treino de modelos pode ser vantajoso quando não são necessárias novas capacidades ou quando o balanço entre adaptação e exploração é positivo. Nesse caso, as curvas de aprendizagem são muito mais estáveis. Esta proposta reúne um conjunto de regras para flexibilizar e simplificar a programação de uma tarefa industrial, baseada numa função de recompensa. As respetivas análises e conclusões resultaram numa publicação proposta para a a conferência ICAR. Uma segunda publicação está a ser preparada.With the joint evolution of Industry and Robotics, manufacturing systems are becoming more complex, resilient, and safer. At the same time, Industry 4.0 answers the requirements to adapt to society demands as fast, seamless, and flexible as possible. Despite bringing intelligence methodologies to the factory floor, the contemporary robotic engineering techniques fail to be flexible or resilient when faced with new configurations of new products or different production parameters. Also, currently used methodologies rarely allow to transfer skills from other tasks. All combined, these factors make the development and updating of robotic systems a cumbersome task that requires extensive resources. To address these limitations, this thesis explores how the use of Learning from Demonstration can contribute the improve robotic engineering. The first goal was to create a manufacturing simulation scenario that could easily transfer to real situations while reacting to Reinforcement Learning Techniques. The second objective was to study how to discretise a complex task. The last goal assessed the impact of reusing pre-trained models in different tasks. The methodology used the robo-gym framework, that connects the OpenAI Gym with the Gazebo physics engine, to create a modified pick and place task, where an object had to be fitted in a goal pose. The training involved expert demonstrations as part of the Learning from Demonstration scope. The algorithm employed was the Generative Adversarial Imitation Learning, which shares both Reinforcement Learning and Inverse Reinforcement Learning characteristics. The first key finding was that task discretisation could be achieved with reward function modelling. It can be done with a default smooth gradient error and positive rewards associated with sub-tasks completion. The positive rewards must be sequentially higher as well as the increments between them. This discretisation approach simplifies the complexity associated with the tasks and boosts performance compared to the sequential modelling approach. Secondly, we have proved that retraining models can be sometimes advantageous even when new skills are not required, or when the trade-off between adaptation and exploration is positive. In that case, the learning curve is more stable. This proposal gathers guidelines to flexibilise and simplify engineering associated with a manufacturing task based on a reward function. The work developed in this thesis resulted in a paper already submitted to ICAR and in a second paper under preparation