用于机器人料箱拣选的高效端到端6-DoF抓取检测
Efficient End-to-End Detection of 6-DoF Grasps for Robotic Bin Picking
AI总结：
针对料箱拣选可达性受限导致可行6-DoF抓取不足的问题，提出基于Power-Spherical分布的端到端概率抓取模型，从单张深度图生成多样无碰撞抓取，并在仿真与真实机器人上达到约90%清空率。
摘要：
料箱拣选是物流、生产或家庭用例中许多机器人系统的重要组成部分。近年来，用于预测多样且未知物体上6-DoF抓取的机器学习方法已展现出可喜进展。然而，现有方法在训练期间仅考虑抓取位置处的单一真实抓取朝向，因此只能预测量有限的抓取朝向，这导致在可达性受限的料箱拣选中可行抓取数量减少。本文提出一种新颖方法，用于为机器人料箱拣选中的平行爪夹持器学习密集且多样的6-DoF抓取。我们引入一种基于Power-Spherical分布的参数化抓取分布模型，使基于所有可能真实样本的训练成为可能。由此，我们还考虑了抓取不确定性，增强模型对含噪输入的鲁棒性。因此，给定单张俯视深度图像，我们的模型能够生成具有多种无碰撞抓取朝向的多样抓取。在仿真和真实机器人料箱拣选装置中的实验评估证明，该模型能够泛化到各种物体类别，在仿真和真实世界实验中实现约90%的物体清空率。我们还优于最先进方法。此外，由于概率抓取分布建模，所提方法即使仅在合成数据集上训练，也无需任何精修步骤即可在真实机器人实验中展现其可用性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
