UC Berkeley

For applications such as warehouse order fulfillment, robot grasps must be robust to uncertainty arising from sensing, mechanics, and control. One way to achieve robustness is to evaluate the performance of candidate grasps by sampling perturbations in shape, pose, and gripper approach and to compute the probability of force closure for each candidate to identify a grasp with the highest expected quality. Since evaluating the quality of each grasp is computationally demanding, prior work has turned to cloud computing. To improve computational efficiency and to extend this work, we consider how Multi-Armed Bandit (MAB) models for optimizing decisions can be applied in this context. We formulate robust grasp planning as a MAB problem and evaluate convergence times towards an optimal grasp candidate using 100 object shapes from the Brown Vision 2D Lab Dataset with 1000 grasp candidates per object. We consider the case where shape uncertainty is represented as a Gaussian process implicit surface (GPIS) with Gaussian uncertainty in pose, gripper approach angle, and coefficient of friction. We find that Thompson Sampling and the Gittins index MAB methods converged to within 3% of the optimal grasp up to 10x faster than uniform allocation and 5x faster than iterative pruning.