Algorithms based on iterative local approximations present a practical approach\ud to optimal control in robotic systems. However, they generally require the temporal\ud parameters (for e.g. the movement duration or the time point of reaching\ud an intermediate goal) to be specified a priori. Here, we present a methodology\ud that is capable of jointly optimizing the temporal parameters in addition to the\ud control command profiles. The presented approach is based on a Bayesian canonical\ud time formulation of the optimal control problem, with the temporal mapping\ud from canonical to real time parametrised by an additional control variable. An approximate\ud EM algorithm is derived that efficiently optimizes both the movement\ud duration and control commands offering, for the first time, a practical approach to\ud tackling generic via point problems in a systematic way under the optimal control\ud framework. The proposed approach, which is applicable to plants with non-linear\ud dynamics as well as arbitrary state dependent and quadratic control costs, is evaluated\ud on realistic simulations of a redundant robotic plant
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