Contextual Policy Search for Linear and Nonlinear Generalization of a Humanoid Walking Controller
作者: Abbas AbdolmalekiNuno LauLuis Paulo ReisJan PetersGerhard Neumann
作者单位: 1DETI / IEETA, University of Aveiro
2DSI, University of Minho
3LIACC, University of Porto
4IAS, TU Darmstadt
5MPI for Intelligent Systems
6CLAS, TU Darmstadt
刊名: Journal of Intelligent & Robotic Systems, 2016, Vol.83 (3-4), pp.393-408
来源数据库: Springer Journal
DOI: 10.1007/s10846-016-0347-y
关键词: Learning humanoids robot locomotionsGeneralizing robot skillsStochastic searchContextual relative entropy policy searchNonlinear policiesNao robot
英文摘要: Abstract(#br)We investigate learning of flexible robot locomotion controllers, i.e., the controllers should be applicable for multiple contexts, for example different walking speeds, various slopes of the terrain or other physical properties of the robot. In our experiments, contexts are desired walking linear speed of the gait. Current approaches for learning control parameters of biped locomotion controllers are typically only applicable for a single context. They can be used for a particular context, for example to learn a gait with highest speed, lowest energy consumption or a combination of both. The question of our research is, how can we obtain a flexible walking controller that controls the robot (near) optimally for many different contexts? We achieve the desired flexibility of...
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影响因子:0.827 (2012)

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