PONG: Probabilistic Object Normals for Grasping via Analytic Bounds on Force Closure Probability

TL;DR

This paper introduces PONG, an analytic method to estimate the probability of force closure in grasping under surface normal uncertainties, enhancing the robustness of precision grasps in simulation and real-world scenarios.

Contribution

PONG provides a novel, analytic approach to quantify grasp robustness considering surface normal uncertainties, improving grasp planning for dexterous manipulation.

Findings

Maximizing PONG yields more robust grasps in challenging geometries.

PONG serves as a well-calibrated, uncertainty-aware grasp quality metric.

The approach is validated in both simulation and real-world experiments.

Abstract

Classical approaches to grasp planning are deterministic, requiring perfect knowledge of an object's pose and geometry. In response, data-driven approaches have emerged that plan grasps entirely from sensory data. While these data-driven methods have excelled in generating parallel-jaw and power grasps, their application to precision grasps (those using the fingertips of a dexterous hand, e.g, for tool use) remains limited. Precision grasping poses a unique challenge due to its sensitivity to object geometry, which allows small uncertainties in the object's shape and pose to cause an otherwise robust grasp to fail. In response to these challenges, we introduce Probabilistic Object Normals for Grasping (PONG), a novel, analytic approach for calculating a conservative estimate of force closure probability in the case when contact locations are known but surface normals are uncertain. We then present a practical application where we use PONG as a grasp metric for generating robust grasps both in simulation and real-world hardware experiments. Our results demonstrate that maximizing PONG efficiently produces robust grasps, even for challenging object geometries, and that it can serve as a well-calibrated, uncertainty-aware metric of grasp quality.