Functional Object-Oriented Network: Considering Robot's Capability in Human-Robot Collaboration
In this work, we explore human-robot collaborative planning using the \emph{functional object-oriented network} (FOON), a graphical knowledge representation for manipulations that can be performed by domestic robots. The knowledge retrieval procedure, used for acquiring the necessary steps (as a task tree) to solve a given problem, is modified to account for weights that reflect the difficulty of performing motions in a universal FOON. These weights are given as success rates, which describe the likelihood of a robot successfully completing the action(s) on its own. However, certain manipulations may be too difficult for it to perform on its own based on its own physical limitations. To make it easier for the robot, a human can assist to the minimal extent needed to perform the activity to completion by identifying those actions with low success rates for the human to do. From our experiments, it is shown that tasks can be executed successfully with the aid of the assistant. Our results show that the best task tree can be found with the adequate chance of success in completing three activities while minimizing the effort needed from the human assistant.