Handling high parameter dimensionality in reinforcement learning with dynamic motor primitives
| Autor: | Colomé Figueras, Adrià, Alenyà Ribas, Guillem|||0000-0002-6018-154X, Torras, Carme|||0000-0002-2933-398X |
|---|---|
| Přispěvatelé: | Institut de Robòtica i Informàtica Industrial, Universitat Politècnica de Catalunya. ROBiri - Grup de Robòtica de l'IRI |
| Předmět: | |
| Zdroj: | Recercat. Dipósit de la Recerca de Catalunya instname Digital.CSIC. Repositorio Institucional del CSIC UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) |
| Popis: | Presentado al IEEE International Conference on Robotics and Automation celebrado en Karlsruhe (Alemania) del 6 al 10 de mayo de 2013. Dynamic Motor Primitives (DMP) are nowadays widely used as movement parametrization for learning trajectories, because of their linearity in the parameters, rescalation robustness and continuity. However, when learning a movement with DMP, where a set of gaussians distributed along the trajectory is used to approximate an acceleration excitation function, a very large number of gaussian approximations need to be performed. Adding them up for all joints yields too many parameters to be explored, thus requiring a prohibitive number of experiments/simulations to converge to a solution with an optimal (locally or globally) reward. We propose here two strategies to reduce this dimensionality: the first is to explore only the most significant directions in the parameter space, and the second is to add a reduced second set of gaussians that should only optimize the trajectory after fixing the gaussians that approximate the demonstrated movement. This work is partially funded by EU Project IntellAct (FP7-269959) and by the Spanish Ministry of Science and Innovation under project PAU+DPI2011-27510. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|