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Picking an item in the presence of other objects can be challenging as itinvolves occlusions and partial views. Given object models, one approach is toperform object pose estimation and use the most likely candidate pose perobject to pick the target without collisions. This approach, however, ignoresthe uncertainty of the perception process both regarding the target's and thesurrounding objects' poses. This work proposes first a perception process for6D pose estimation, which returns a discrete distribution of object poses in ascene. Then, an open-loop planning pipeline is proposed to return safe andeffective solutions for moving a robotic arm to pick, which (a) minimizes theprobability of collision with the obstructing objects; and (b) maximizes theprobability of reaching the target item. The planning framework models thechallenge as a stochastic variant of the Minimum Constraint Removal (MCR)problem. The effectiveness of the methodology is verified given both simulatedand real data in different scenarios. The experiments demonstrate theimportance of considering the uncertainty of the perception process in terms ofsafe execution. The results also show that the methodology is more effectivethan conservative MCR approaches, which avoid all possible object posesregardless of the reported uncertainty.

Safe and Effective Picking Paths in Clutter given Discrete Distributions of Object Poses