Multi-armed bandit models for 2D grasp planning with uncertainty

Abstract — For applications such as warehouse order fulfill-ment, 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 com-pute the probability of force closure for each candidate to iden-tify 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. I

Estimating Part Tolerance Bounds Based on Adaptive Cloud-Based Grasp Planning with Slip

Abstract — We explore setting bounds on part tolerances based on an adaptive Cloud-based algorithm to estimate lower bounds on achieving force closure during grasping. We consider the most common robot gripper: a pair of thin parallel jaws, and a conservative class of push-grasps allowing slip that can enhance part alignment for parts that can be modeled as extruded polygons. The grasp analysis algorithm takes as input a set of candidate grasps and perturbations of a nominal part shape. We define a grasp quality metric based on a lower bound on the probability of achieving force closure. We present two extensions to our previous highly-parallelizable algorithm that adaptively reduce the number of grasp evaluations and improve the lower bound by including slip. We develop a procedure for finding the effect of increasing tolerance in vertices on grasp quality, which allows part tolerances to be bounded to ensure minimum grasp quality levels. We find that including slip improves grasp quality estimates by 16%, and our adaptive extension reduces grasp evaluations by 91.5 % while maintaining 92.6 % of grasp quality. I