Learning 6-DoF Task-oriented Grasp Detection via Implicit Estimation and Visual Affordance

Understand

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.