On the Importance of Label Encoding and Uncertainty Estimation for Robotic Grasp Detection

Automated grasping of arbitrary objects is an essential skill for many applications such as smart manufacturing and human robot interaction. This makes grasp detection a vital skill for automated robotic systems. Recent work in model-free grasp detection uses point cloud data as input and typically outperforms the earlier work on RGB(D)-based methods. We show that RGB(D)-based methods are being underestimated due to suboptimal label encodings used for training. Using the evaluation pipeline of the GraspNet-1Billion dataset, we investigate different encodings and propose a novel encoding that significantly improves grasp detection on depth images. Additionally, we show shortcomings of the 2D rectangle grasps supplied by the GraspNet-1Billion dataset and propose a filtering scheme by which the ground truth labels can be improved significantly. Furthermore, we apply established methods for uncertainty estimation on our trained models since knowing when we can trust the model's decisions provides an advantage for real-world application. By doing so, we are the first to directly estimate uncertainties of detected grasps. We also investigate the applicability of the estimated aleatoric and epistemic uncertainties based on their theoretical properties. Additionally, we demonstrate the correlation between estimated uncertainties and grasp quality, thus improving selection of high quality grasp detections. By all these modifications, our approach using only depth images can compete with point-cloud-based approaches for grasp detection despite the lower degree of freedom for grasp poses in 2D image space.