58 research outputs found
Model-based contextual policy search for data-efficient generalization of robot skills
In robotics, lower-level controllers are typically used to make the robot solve a specific task in a fixed context. For example, the lower-level controller can encode a hitting movement while the context defines the target coordinates to hit. However, in many learning problems the context may change between task executions. To adapt the policy to a new context, we utilize a hierarchical approach by learning an upper-level policy that generalizes the lower-level controllers to new contexts. A common approach to learn such upper-level policies is to use policy search. However, the majority of current contextual policy search approaches are model-free and require a high number of interactions with the robot and its environment. Model-based approaches are known to significantly reduce the amount of robot experiments, however, current model-based techniques cannot be applied straightforwardly to the problem of learning contextual upper-level policies. They rely on specific parametrizations of the policy and the reward function, which are often unrealistic in the contextual policy search formulation. In this paper, we propose a novel model-based contextual policy search algorithm that is able to generalize lower-level controllers, and is data-efficient. Our approach is based on learned probabilistic forward models and information theoretic policy search. Unlike current algorithms, our method does not require any assumption on the parametrization of the policy or the reward function. We show on complex simulated robotic tasks and in a real robot experiment that the proposed learning framework speeds up the learning process by up to two orders of magnitude in comparison to existing methods, while learning high quality policies
Advances in Robotics: FIRA RoboWorld Congress 2009 Incheon, Korea, August 16-20, 2009 Proceedings - Preface
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)5744 LNCS
Improved particle filter in sensor fusion for tracking randomly moving object
10.1109/TIM.2006.881569IEEE Transactions on Instrumentation and Measurement5551823-1832IEIM
Multiple targets tracking by optimized particle filter based on multi-scan JPDA
Conference Record - IEEE Instrumentation and Measurement Technology Conference1303-308CRII
Interacting MCMC particle filter for tracking maneuvering target
10.1016/j.dsp.2009.08.011Digital Signal Processing: A Review Journal202561-574DSPR
Improved particle filter in sensor fusion for tracking random moving object
10.1109/IMTC.2004.1351092Conference Record - IEEE Instrumentation and Measurement Technology Conference1476-481CRII
International Journal of Humanoid Robotics: Editorial
10.1142/S0219843609001966International Journal of Humanoid Robotics64v-v
Graph matching based hand posture recognition using neuro-biologically inspired features
10.1109/ICARCV.2010.570735211th International Conference on Control, Automation, Robotics and Vision, ICARCV 20101151-115
Identifying social groups in pedestrian crowd videos
10.1109/ICAPR.2015.7050677ICAPR 2015 - 2015 8th International Conference on Advances in Pattern Recognitio
Fuzzy-rough discriminative feature selection and classification algorithm, with application to microarray and image datasets
10.1016/j.asoc.2011.01.013Applied Soft Computing Journal1143429-344
- …