Action-related place-based mobile manipulation

In mobile manipulation, the position to which the robot navigates has a large influence on the ease with which a subsequent manipulation action can be performed. Whether a manipulation action succeeds depends on many factors, such as the robot´s hardware configuration, the controllers the robot uses to achieve navigation and manipulation, the task context, and uncertainties in state estimation. In this paper, we present `ARPLACE´, an action-related place which takes these factors, and the context in which the actions are performed into account. Through experience-based learning, the robot first learns a so-called generalized success model, which discerns between positions from which manipulation succeeds or fails. On-line, this model is used to compute a ARPLACE, a probability distribution that maps positions to a predicted probability of successful manipulation, and takes the uncertainty in the robot and object´s position into account. In an empirical evaluation, we demonstrate that using ARPLACEs for least-commitment navigation improves the success rate of subsequent manipulation tasks substantially.