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This paper proposes an intelligent mobility aid framework aimed at mitigating the impact of cognitive and/or physical user deficiencies by performing suitable mobility assistance with minimum interference. To this end, a user action model using Gaussian Process Regression (GPR) is proposed to encapsulate the probabilistic and nonlinear relationships among user action, state of the environment and user intention. Moreover, exploiting the analytical tractability of the predictive distributiondoi:10.1109/roman.2015.7333580 dblp:conf/ro-man/MatsubaraMTPS15 fatcat:a7f3czrpnnbkdntkcg5af5ryeu