Adaptive Autonomous Grasp Selection via Pairwise Ranking

Autonomous grasp selection for robot pick-and-place applications makes use of either empirical methods leveraging object databases, which generate grasps for specific objects at the initial cost of modeling effort, or analytical methods, which generalize to novel objects but fail on object subsets that require specific grasping strategies not captured by the algorithm. We introduce a grasp selection algorithm that ranks grasp candidates with a set of grasp metrics augmented with object features, creating an approach that adapts its strategies based on user-specified grasp preferences. We formulate grasp selection as a pairwise ranking problem, which significantly reduces data collection compared to traditional grasp ranking methods and generalizes to novel objects. Our approach outperforms a state-of-the-art grasp calculation baseline and a pointwise ranking formulation of the same problem.