Enabling Grasp Synthesis Approaches to Task-Oriented Grasping Considering the End-State Comfort and Confidence Effects

Choosing a good grasp is fundamental for accom- plishing robotic grasping and manipulation tasks. Typically, the grasp synthesis is addressed separately from the planning phase, which can lead to failures during the execution of the task. In ad- dition, most of the current grasping approaches privilege stability metrics, providing unsuitable grasps for executing subsequent tasks. The proposed work presents a framework for high-level reasoning to select the best-suited grasp depending on the task. The best grasp is chosen among a set of grasp candidates by solving an optimization problem, considering the environmental constraints, and guaranteeing the end-state comfort and the confidence effects for the task, similar to human behavior. The framework leverages Generalized Bender Decomposition to decouple the main non-linear optimization problem into sub- problems, thus presenting a modular structure. The method is validated with an experimental campaign using three different state-of-the-art grasping algorithms and three low-level motion planners in three different types of tasks: pick-and-place in a constrained environment, handover/tool-use, and object re- orientation. The experiments show that the proposed approach is able to find the best grasp, or at least one feasible, among the provided candidates for each task.