Quick learning of cause-effects relevant for robot action
In this work we propose a new paradigm for the rapid learning of cause-effect relations relevant for task execution. Learning occurs automatically from action experiences by means of a novel constructive learning approach designed for applications where there is no previous knowledge of the task or...
| Autores: | , , |
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| Tipo de recurso: | informe técnico |
| Fecha de publicación: | 2010 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/12364 |
| Acceso en línea: | https://hdl.handle.net/2117/12364 |
| Access Level: | acceso abierto |
| Palabra clave: | Machine learning learning (artificial intelligence) service robots. Aprenentatge automàtic Classificació INSPEC::Cybernetics::Artificial intelligence::Learning (artificial intelligence) Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
| Sumario: | In this work we propose a new paradigm for the rapid learning of cause-effect relations relevant for task execution. Learning occurs automatically from action experiences by means of a novel constructive learning approach designed for applications where there is no previous knowledge of the task or world model, examples are provided on-line during run time, and the number of examples is small compared to the number of incoming experiences. These limitations pose obstacles for the existing constructive learning methods, where on-line learning is either not considered, a significant amount of prior knowledge has to be provided, or a large number of experiences or training streams are required. The system is implemented and evaluated in a humanoid robot platform using a decision-making framework that integrates a planner, the proposed learning mechanism, and a human teacher that supports the planner in the action selection. Results demonstrate the feasibility of the system for decision making in robotic applications. |
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