Robot learning of container-emptying skills through haptic demonstration
Locally weighted learning algorithms are suitable strategies for trajectory learning and skill acquisition, in the context of programming by demonstration. Input streams other than visual information, as used in most applications up to date, reveal themselves as quite useful in trajectory learning e...
| Autores: | , , |
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| Tipo de recurso: | otro |
| Fecha de publicación: | 2009 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/30059 |
| Acceso en línea: | http://hdl.handle.net/10261/30059 |
| Access Level: | acceso abierto |
| Palabra clave: | Learning by demonstration Locally weighted learning Manipulation skills Intelligent robots and autonomous agents Manipulators (Mechanism) Robotics |
| Sumario: | Locally weighted learning algorithms are suitable strategies for trajectory learning and skill acquisition, in the context of programming by demonstration. Input streams other than visual information, as used in most applications up to date, reveal themselves as quite useful in trajectory learning experiments where visual sources are not available. In this work we have used force/torque feedback through a haptic device for teaching a teleoperated robot to empty a rigid container. Structure vibrations and container inertia appeared to considerably disrupt the sensing process, so a filtering algorithm had to be devised. Then, memory-based LWPLS (locally weighted partial least squares) and non-memory-based LWPR (locally weighted projection regression) algorithms were implemented, their comparison leading to very similar results, with the same pattern as regards to both the involved robot joints and the different initial experimental conditions. Tests where the teacher was instructed to follow a strategy compared to others where he was not lead to useful conclusions that permit devising the new research stages, where the taught motion will be refined by autonomous robot rehearsal through reinforcement learning. |
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