基于深度学习的机器人抓取检测 采用康奈尔大学抓取数据集
We consider the problem of detecting robotic grasps
in an RGB-D view of a scene containing objects. In this work,
we apply a deep learning approach to solve this problem, which
avoids time-consuming hand-design of features. This presents two
main challenges. First, we need to evaluate a huge number of
candidate grasps. In order to make detection fast and robust,
we present a two-step cascaded system with two deep networks,
where the top detections from the first are re-evaluated by
the second. The first network has fewer features, is faster to
run, and can effectively prune out unlikely candidate grasps.
The second, with more features, is slower but has to run
only on the top few detections. Second, we need to handle
multimodal inputs effectively, for which we present a method
that applies structured regularization on the weights based on
multimodal group regularization. We show that our method
improves performance on an RGBD robotic grasping dataset,
and can be used to successfully execute grasps on two different
robotic platforms.
