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Deep learning for detecting robotic grasps
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DOI：10.1177/0278364914549607.png)
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摘要
En 中文
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.
Keyword:
Robotic grasping
deep learning
RGB-D multi-modal data
Baxter
PR2
3D feature learning
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Rigid 3D geometry matching for grasping of known objects in cluttered scenes用于在杂乱场景中抓取已知对象的刚性3D几何匹配
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