  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.
