000166748 001__ 166748
000166748 005__ 20190316235141.0
000166748 037__ $$aCONF
000166748 245__ $$aLearning User Habits for Semi-Autonomous Navigation Using Low Throughput Interfaces
000166748 269__ $$a2011
000166748 260__ $$c2011
000166748 336__ $$aConference Papers
000166748 520__ $$aThis paper presents a semi-autonomous navigation strategy aimed at the control of assistive devices (e.g. intelligent wheelchair) using low throughput interfaces. A mobile robot proposes the most probable action, as analyzed from the environment, to a human user who can either accept or reject the proposition. In case of rejection, the robot will propose another action, until both entities agree on what needs to be done. In a known environment, the system infers the intended goal destination based on the first executed actions. Furthermore, we endowed the system with learning capabilities, so as to learn the user habits depending on contextual information (e.g. time of the day or a phone rings). This additional knowledge allows the robot to anticipate the user intention and propose appropriate actions, or goal destinations.
000166748 6531_ $$aMobile Robot Control
000166748 6531_ $$aHuman-Robot Interaction
000166748 6531_ $$aGoal Inference
000166748 6531_ $$aHabit Learning
000166748 6531_ $$a[BACS]
000166748 700__ $$aPerrin, Xavier
000166748 700__ $$aColas, Francis
000166748 700__ $$0241256$$g137762$$aChavarriaga, Ricardo
000166748 700__ $$aPradalier, Cédric
000166748 700__ $$0240030$$g149175$$aMillán, José del R.
000166748 700__ $$aSiegwart, Roland
000166748 7112_ $$dOctober 9-12, 2011.$$cAnchorage, Alaska, USA$$aIEEE Int Conf Systems, Man, and Cybernetics (IEEE SMC 2011)
000166748 8564_ $$uhttps://infoscience.epfl.ch/record/166748/files/PerrinCoChPrMiSi11.pdf$$zn/a$$s626561$$yn/a
000166748 909C0 $$xU12103$$0252018$$pCNBI
000166748 909C0 $$pCNP$$xU12599$$0252517
000166748 909CO $$qGLOBAL_SET$$pconf$$pSTI$$ooai:infoscience.tind.io:166748
000166748 917Z8 $$x137762
000166748 917Z8 $$x137762
000166748 917Z8 $$x137762
000166748 937__ $$aEPFL-CONF-166748
000166748 973__ $$rNON-REVIEWED$$sACCEPTED$$aEPFL
000166748 980__ $$aCONF