A Probabilistic Framework for Uncertainty-Aware High-Accuracy Precision Grasping of Unknown Objects

Grasping is a fundamental skill for robots which work for manipulation tasks. Grasping of unknown objects remains a big challenge. Precision grasping of unknown objects is even harder. Due to imperfection of sensor measurements and lack of prior knowledge of objects, robots have to handle the uncertainty effectively. In previous work (Chen and Wichert 2015), we use a probabilistic framework to tackle precision grasping of model-based objects. In this paper, we extend the probabilistic framework to tackle the problem of precision grasping of unknown objects. We first propose an object model called probabilistic signed distance function (p-SDF) to represent unknown object surface. p-SDF models measurement uncertainty explicitly and allows measurement from multiple sensors to be fused in real time. Based on the surface representation, we propose a model to evaluate the likelihood of grasp success for antipodal grasps. This model uses four heuristics to model the condition of force closure and perceptual uncertainty. A two step simulated annealing approach is further proposed to search and optimize a precision grasp. We use the object representation as a bridge to unify grasp synthesis and grasp execution. Our grasp execution is performed in a closed-loop, so that robots can actively reduce the uncertainty and react to external perturbations during a grasping process. We perform extensive grasping experiments using real world challenging objects and demonstrate that our method achieves high robustness and accuracy in grasping unknown objects.