Grasp benchmark · Parallel-jaw (Franka)
VGN
6-DoFvolumetricreal-timeclutter
The Volumetric Grasping Network (VGN) predicts 6-DoF grasps directly from a Truncated Signed Distance Function (TSDF) of the scene in a single forward pass, making it fast enough for real-time clutter clearing.
It established the volumetric / TSDF input that GIGA later extended with implicit representations.
Primary source →At a glance
- Source
- Breyer et al., CoRL 2020
- Year
- 2020
- Scale
- ~2M synthetic grasps · 303 training meshes
- Gripper
- Parallel-jaw (Franka)
- Modality
- TSDF (from depth)
- Best-known
- HW: 80% grasp success · 92% clutter clearance · ~10 ms plan
Key results
- HW: 80% grasp success · 92% clutter clearance
- Plans a grasp in roughly 10 ms
- Volumetric TSDF input, single forward pass
SIM = simulation result · HW = physical hardware. Image-wise accuracy is detection quality, not real-robot pick success. Figures cited from Breyer et al., CoRL 2020.