Deep learning for detecting robotic grasps
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- 16 March 2015
- journal article
- research article
- Published by SAGE Publications in The International Journal of Robotics Research
- Vol. 34 (4-5), 705-724
- https://doi.org/10.1177/0278364914549607
Abstract
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.Keywords
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This publication has 17 references indexed in Scilit:
- Data-Driven Grasp Synthesis—A SurveyIEEE Transactions on Robotics, 2013
- 3-D Mapping With an RGB-D CameraIEEE Transactions on Robotics, 2013
- Learning to place new objects in a sceneThe International Journal of Robotics Research, 2012
- Rigid 3D geometry matching for grasping of known objects in cluttered scenesThe International Journal of Robotics Research, 2012
- The MOPED framework: Object recognition and pose estimation for manipulationThe International Journal of Robotics Research, 2011
- Acoustic Modeling Using Deep Belief NetworksIEEE Transactions on Audio, Speech, and Language Processing, 2011
- Reducing the Dimensionality of Data with Neural NetworksScience, 2006
- Robust Visual ServoingThe International Journal of Robotics Research, 2003
- Manipulation of unmodeled objects using intelligent grasping schemesIEEE Transactions on Fuzzy Systems, 2003
- Topographic Independent Component AnalysisNeural Computation, 2001