论文标题
通过隐式估计和视觉负担来学习6-DOF任务的GRASP检测
Learning 6-DoF Task-oriented Grasp Detection via Implicit Estimation and Visual Affordance
论文作者
论文摘要
当前，面向任务的GRASP检测方法主要基于像素级负担能力检测和语义分割。这些像素级方法在很大程度上依赖于2D负担性面罩的准确性，并且产生的抓手候选者仅限于小型工作区。为了减轻这些局限性，我们首先构建了一种基于可负担的GRASP数据集，并提出了一个6-DOF任务的GRASP检测框架，该框架将观察到的对象点云作为输入，并预测不同任务的多种6-DOF GRASP姿势。具体而言，我们在此框架中的隐式估计网络和视觉负担能力网络可以直接预测粗略的抓取候选者，并分别针对每个潜在任务提供相应的3D负担热图。此外，从粗grasps的抓地力分数与热图值结合在一起，以产生更准确，更精确的候选者。与我们的仿真数据集中现有和新颖对象的基线相比，我们提出的框架显示出显着改善。尽管我们的框架是根据模拟对象和环境进行训练的，但是当对象被随机放置在支撑面上时，最终生成的抓取候选物可以在实际机器人实验中准确稳定。
Currently, task-oriented grasp detection approaches are mostly based on pixel-level affordance detection and semantic segmentation. These pixel-level approaches heavily rely on the accuracy of a 2D affordance mask, and the generated grasp candidates are restricted to a small workspace. To mitigate these limitations, we first construct a novel affordance-based grasp dataset and propose a 6-DoF task-oriented grasp detection framework, which takes the observed object point cloud as input and predicts diverse 6-DoF grasp poses for different tasks. Specifically, our implicit estimation network and visual affordance network in this framework could directly predict coarse grasp candidates, and corresponding 3D affordance heatmap for each potential task, respectively. Furthermore, the grasping scores from coarse grasps are combined with heatmap values to generate more accurate and finer candidates. Our proposed framework shows significant improvements compared to baselines for existing and novel objects on our simulation dataset. Although our framework is trained based on the simulated objects and environment, the final generated grasp candidates can be accurately and stably executed in real robot experiments when the object is randomly placed on a support surface.
