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Quick learning of cause-effects relevant for robot action
Agostini, Alejandro Gabriel; Wörgötter, Florentin; Torras, Carme
Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics; Universitat Politècnica de Catalunya. Institut de Robòtica i Informàtica Industrial
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.
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
Machine learning
learning (artificial intelligence) service robots.
Aprenentatge automàtic
Classificació INSPEC::Cybernetics::Artificial intelligence::Learning (artificial intelligence)
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
Artículo - Borrador
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