English   español  
Por favor, use este identificador para citar o enlazar a este item: http://hdl.handle.net/10261/166773
logo share SHARE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:

Demonstration-free contextualized probabilistic movement primitives, further enhanced with obstacle avoidance

AutorColomé, Adrià ; Torras, Carme
Fecha de publicación2017
EditorInstitute of Electrical and Electronics Engineers
CitaciónIEEE/RSJ International Conference on Intelligent Robots and Systems: 3190-3195 (2017)
ResumenMovement Primitives (MPs) have been widely used over the last years for learning robot motion tasks with direct Policy Search (PS) reinforcement learning. Among them, Probabilistic Movement Primitives (ProMPs) are a kind of MP based on a stochastic representation over sets of trajectories, which benefits from the properties of probability operations. However, the generation of such ProMPs requires a set of demonstrations to capture motion variability. Additionally, using context variables to modify trajectories coded as MPs is a popular approach nowadays in order to adapt motion to environmental variables. This paper proposes a contextual representation of ProMPs that allows for an easy adaptation to changing situations through context variables, by reparametrizing motion with them. Moreover, we propose a way of initializing contextual trajectories without the need of real robot demonstrations, by setting an initial position, a final position, and a number of trajectory interest points, where the contextual variables are evaluated. The parametrizations obtained show to be accurate while relieving the user from the need of performing costly computations such as conditioning. Additionally, using this contextual representation, we propose a simple yet effective quadratic optimization-based obstacle avoidance method for ProMPs. Experiments in simulation and on a real robot show the promise of the approach.
DescripciónTrabajo presentado a la IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), celebrada en Vancouver (Canada) del 24 al 28 de septiembre de 2017.
Versión del editorhttps://doi.org/10.1109/IROS.2017.8206151
Identificadoresdoi: 10.1109/IROS.2017.8206151
isbn: 978-1-5386-2683-2
Aparece en las colecciones: (IRII) Libros y partes de libros
Ficheros en este ítem:
Fichero Descripción Tamaño Formato  
primitiveavoid.pdf1,57 MBUnknownVisualizar/Abrir
Mostrar el registro completo

NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.