Grasp benchmark · Parallel-jaw
Dex-Net 2.0
parallel-jawGQ-CNNsynthetic trainingsim-to-real
Dex-Net 2.0 introduced the Grasp-Quality CNN (GQ-CNN): a network that scores candidate parallel-jaw grasps from a single depth image, trained on 6.7 million synthetic point-cloud / grasp pairs generated from a large dataset of 3D models with analytic robustness labels.
It was a landmark demonstration that a model trained entirely in simulation could transfer to a physical robot with high reliability, anchoring the synthetic-training paradigm that still dominates grasp learning.
Primary source →At a glance
- Source
- Mahler et al., RSS 2017
- Year
- 2017
- Scale
- 6.7M synthetic point clouds + grasps from thousands of 3D models
- Gripper
- Parallel-jaw
- Modality
- Depth
- Best-known
- HW: 93% on adversarial · 99% precision on 40 novel objects (YuMi)
Key results
- HW: 93% success on a set of known adversarial objects (ABB YuMi)
- HW: 99% precision on 40 novel objects
- Introduced the GQ-CNN grasp-quality network
SIM = simulation result · HW = physical hardware. Image-wise accuracy is detection quality, not real-robot pick success. Figures cited from Mahler et al., RSS 2017.