Decentralized approaches for cooperative grasp planning

Mobile manipulation of large objects can benefit greatly from the use of multiple cooperating robots. Multi-robot coordination of decentralized systems is, however, challenging due to the nature of such systems. For this reason, many planning problems are yet unexplored. This paper proposes two decentralized approaches for cooperative grasp planning. In our setting, agents do not have information about the embodiments of other agents and each agent thus needs to plan its own actions. The proposed approaches are based on analysing the traditional grasp quality metrics in a probabilistic context which compensates for the incomplete information. Results from simulation experiments demonstrate that the probabilistic approaches clearly improve performance over a baseline approach where the grasps are planned independently. Moreover, their performance is close to that of the optimal centralized approach.