Iterative affordance learning with adaptive action generation
| Autor: | Christophe Gonzales, Stéphane Doncieux, Carlos Maestre, Ghanim Mukhtar |
|---|---|
| Přispěvatelé: | Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), DECISION, Laboratoire d'Informatique de Paris 6 (LIP6), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), AMAC |
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Computer science 02 engineering and technology Object (computer science) Electronic mail [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Task (project management) 03 medical and health sciences 020901 industrial engineering & automation 0302 clinical medicine Human–computer interaction Task analysis Trajectory [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO] Robot Affordance Set (psychology) 030217 neurology & neurosurgery |
| Zdroj: | ICDL-EPIROB International Conference on Development and Learning (ICDL) and the International Conference on Epigenetic Robotics (EpiRob) International Conference on Development and Learning (ICDL) and the International Conference on Epigenetic Robotics (EpiRob), Sep 2017, Lisbon, Portugal |
| DOI: | 10.1109/devlrn.2017.8329832 |
| Popis: | International audience; A robot designer can provide a robot with knowledge to perform tasks on an environment. However, this approach can limit the achievement of future tasks executed by the robot. Providing it with the ability to develop its own skills paves the way for robots that are not limited by design. In this work a task consists in reproducing a given set of effects on an object. A robot must accomplish this task with limited information about the object, learning affordances to reproduce the effects, increasing this information throughout consecutive interactions with the object. We propose a method named Adaptive Affor-dance Learning (A 2 L) which endows a robot with the capacity to learn affordances associated to an object, both adapting the robot's actions to the object position, and increasing the robot's information about the object when needed. This paper presents two main contributions: first, an online adaption of the robot actions to interact with the object, decomposing each action into a sequence of movements, adapting each movement, in a close loop, to the object position; and second, to increase the information about the object, we propose an iterative process that alternates between (1) exploration of the environment interacting with the object, (2) affordance acquisition and (3) affordance validation. These contributions are assessed in two experiments where a simulated Baxter robot learns to push a box to different positions on a table. |
| Databáze: | OpenAIRE |
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