Action-related place-based mobile manipulation

14 years 7 months ago

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— 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 dem...

Freek Stulp, Andreas Fedrizzi, Michael Beetz

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