Instrucción de tareas a un robot con retroalimentación en línea proporcionada por voz
Robots are increasingly common in our daily lives and therefore they need to work in environments shared with humans. In service robotics they need to adapt to changing environments, interact naturally with non-expert users and also work with time restrictions. Trying to solve these needs, some meth...
| Autor: | |
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| Tipo de recurso: | tesis de maestría |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2010 |
| País: | México |
| Institución: | Instituto Nacional de Astrofísica, Óptica y Electrónica |
| Repositorio: | Repositorio Institucional del INAOE |
| Idioma: | español |
| OAI Identifier: | oai:inaoe.repositorioinstitucional.mx:1009/606 |
| Acceso en línea: | http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/606 |
| Access Level: | acceso abierto |
| Palabra clave: | info:eu-repo/classification/Aprendizaje/Learning info:eu-repo/classification/Habla/Speech info:eu-repo/classification/Robots inteligentes/Intelligent robots info:eu-repo/classification/Robots/Robots info:eu-repo/classification/cti/1 info:eu-repo/classification/cti/12 info:eu-repo/classification/cti/1203 |
| Sumario: | Robots are increasingly common in our daily lives and therefore they need to work in environments shared with humans. In service robotics they need to adapt to changing environments, interact naturally with non-expert users and also work with time restrictions. Trying to solve these needs, some methods have been proposed to program robots for those situations, between them, reinforcement learning and learning by demonstration. These methods have been widely used and although good results have been obtained with them, they have some problems that should be solved. Reinforcement learning has long times for training and some problems with methods working in continuous spaces, which require a lot of experience and therefore spend so long, and sometimes they do not converge. Reward shaping has been used in reinforcement learning algorithms to accelerate learning, however, it requires a priori domain knowledge and therefore, it is static because it can not be adjusted during the learning process. On the other hand, the success of learning by demonstration is based on the knowledge and the abilities of the user who provides examples to the robot, and also this learning does not cover all the space of possibilities in the task domain. Addressing these problems, this thesis presents an algorithm of reinforcement learning based on Sarsa(λ ), with initial task demonstration by voice, and includes additional on-line feedback to the traditional reinforcements, feedback is provided through commands and qualifiers by voice. Speech provides a way of natural instruction, accessible to non-expert users, and its inclusion works as a reward shaping method in the learning algorithm. Unless the most widely used reward shaping approaches, additional feedback provided by voice is variable along time, so it works as a dynamic method of reward shaping that does not need a prior knowledge or designs (of functions). At the same time a new simple representation to work on-line with continuous spaces is proposed. Experiments done with navigation tasks and one handling task show how the proposed algorithm works with continuous spaces and on-line feedback, and how learning time can be reduced significantly compared to traditional reinforcement learning algorithms, obtaining very similar policies. |
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