Data-driven approach for grasping shows significant advance recently. But
these approaches usually require much training data. To increase the efficiency
of grasping data collection, this paper presents a novel grasp training system
including the whole pipeline from data collection to model inference. The
system can collect effective grasp sample with a corrective strategy assisted
by antipodal grasp rule, and we design an affordance interpreter network to
predict pixelwise grasp affordance map. We define graspability, ungraspability
and background as grasp affordances. The key advantage of our system is that
the pixel-level affordance interpreter network trained with only a small number
of grasp samples under antipodal rule can achieve significant performance on
totally unseen objects and backgrounds. The training sample is only collected
in simulation. Extensive qualitative and quantitative experiments demonstrate
the accuracy and robustness of our proposed approach. In the real-world grasp
experiments, we achieve a grasp success rate of 93% on a set of household items
and 91% on a set of adversarial items with only about 6,300 simulated samples.
We also achieve 87% accuracy in clutter scenario. Although the model is trained
using only RGB image, when changing the background textures, it also performs
well and can achieve even 94% accuracy on the set of adversarial objects, which
outperforms current state-of-the-art methods.

本文提出了一个针对数据收集到模型推理整个过程的新型夹持训练系统，通过抗脊夹持规则来获取有效的夹持样本，并设计了一个效益解释器网络来预测像素级的夹持效益地图，该模型只在模拟环境中训练，结果表明当模型面对全新的物体和背景时，它能做出很好的夹取决策，夹取成功率为 93%。