Efficient End-to-End Detection of 6-DoF Grasps for Robotic Bin Picking

TL;DR

This paper introduces a novel probabilistic approach for predicting diverse, collision-free 6-DoF grasps in robotic bin picking, improving robustness and generalization over existing methods, and achieving high success rates in simulation and real-world tests.

Contribution

It proposes a parameterized grasp distribution model based on Power-Spherical distributions that enables learning from all possible grasp orientations, enhancing diversity and robustness.

Findings

Achieves around 90% object clearing rate in experiments.

Outperforms state-of-the-art grasp prediction methods.

Works effectively with synthetic training data in real robot experiments.

Abstract

Bin picking is an important building block for many robotic systems, in logistics, production or in household use-cases. In recent years, machine learning methods for the prediction of 6-DoF grasps on diverse and unknown objects have shown promising progress. However, existing approaches only consider a single ground truth grasp orientation at a grasp location during training and therefore can only predict limited grasp orientations which leads to a reduced number of feasible grasps in bin picking with restricted reachability. In this paper, we propose a novel approach for learning dense and diverse 6-DoF grasps for parallel-jaw grippers in robotic bin picking. We introduce a parameterized grasp distribution model based on Power-Spherical distributions that enables a training based on all possible ground truth samples. Thereby, we also consider the grasp uncertainty enhancing the model's robustness to noisy inputs. As a result, given a single top-down view depth image, our model can generate diverse grasps with multiple collision-free grasp orientations. Experimental evaluations in simulation and on a real robotic bin picking setup demonstrate the model's ability to generalize across various object categories achieving an object clearing rate of around $90 \%$ in simulation and real-world experiments. We also outperform state of the art approaches. Moreover, the proposed approach exhibits its usability in real robot experiments without any refinement steps, even when only trained on a synthetic dataset, due to the probabilistic grasp distribution modeling.