Towards Precise Robotic Grasping by Probabilistic Post-grasp Displacement Estimation

TL;DR

This paper introduces a method for precise robotic grasping using neural networks to predict grasp robustness and post-grasp displacement, enabling accurate manipulation in industrial settings without additional real-world training.

Contribution

The paper presents a novel approach combining neural networks for robustness prediction and displacement estimation, trained solely in simulation, to improve real-world robotic grasping accuracy.

Findings

Displacement estimator achieves 0.68cm and 3.42° mean errors on new objects.

Networks trained in simulation successfully deployed on real robots.

Method enhances grasp precision without further fine-tuning.

Abstract

Precise robotic grasping is important for many industrial applications, such as assembly and palletizing, where the location of the object needs to be controlled and known. However, achieving precise grasps is challenging due to noise in sensing and control, as well as unknown object properties. We propose a method to plan robotic grasps that are both robust and precise by training two convolutional neural networks - one to predict the robustness of a grasp and another to predict a distribution of post-grasp object displacements. Our networks are trained with depth images in simulation on a dataset of over 1000 industrial parts and were successfully deployed on a real robot without having to be further fine-tuned. The proposed displacement estimator achieves a mean prediction errors of 0.68cm and 3.42deg on novel objects in real world experiments.