adaptive robot learning in a non-stationary environmentnew

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1、Adaptiverobotlearninginanon-stationaryenvironmentKaryFrämlingHelsinkiUniversityofTechnology,DepartmentofComputerScience,FI-02015HUT,FinlandKary.Framling@hut.fiAbstract.Adaptivecontrolischallenginginreal-worldapplicationssuchasrobotics.Learninghastoberapidenoughtobeperformedinrealtimeandtoavo

2、iddamagetotherobot.Modelsusinglinearfunctionapproximationareinterestinginsuchtasksbecausetheyofferrapidlearningandhavesmallmemoryandprocessingrequirements.Thismakesthemsuitableasadaptivecontrollersinnon-stationaryenvironments,especiallywhenthecontrollerneedstobeanembeddedsystem.Experimentswi

3、thalight-seekingrobotillustratehowtherobotadaptstotheenvironmentbyReinforcementLearningwheretherobotcollectstrainingsamplesbyexploringtheenvironment.1IntroductionTheuseofmachinelearninginreal-worldcontrolapplicationsischallenging.Real-worldtasks,suchasthoseusingrealrobots,involvenoisecomingf

4、romsensors,non-deterministicactionsanduncontrollablechangesintheenvironment.Inrobotics,learningmustberelativelyrapidandpossibletoperformwithoutcausingdamagetotherobot.Onlyinformationthatisavailablefromrobotsensorscanbeusedforlearning.Thismeansthatthelearningmethodshavetobeabletohandlepartial

5、lymissinginformationandsensornoise,whichmaybedifficulttotakeintoaccountinsimulatedenvironments.Artificialneuralnetworks(ANN)areawell-knowntechniqueformachinelearninginnoisyenvironments.Inrealroboticsapplications,however,ANNlearningmaybecometooslowtobepractical.One-layerlinearfunctionapproxim

6、ationANNs(oftencalledAdalines[7])offerfastertrainingthannon-linearANNsandtheirconvergencetoanoptimalsolutioncanusuallybeguaranteed.Thesearepropertiesthatareparticularlyusefulinnon-stationaryenvironmentsthatrequirerapidadaptation,especiallyiftherobothastoexploretheenvironmentandcollecttrainin

7、gsamplesbyitself.Learningbyautonomousexplorationoftheenvironmentisoftenperformedusingreinforcementlearning(RL)methods.Finally,thelimitedmemory-andcomputingpowerneedsofAdalinesmakethemeasytouseinembeddedsystems.Thestructureofthispaperisasfollows.Sec