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Deep learning for detecting robotic grasps
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-consumingExpand
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Structural-RNN: Deep Learning on Spatio-Temporal Graphs
Deep Recurrent Neural Network architectures, though remarkably capable at modeling sequences, lack an intuitive high-level spatio-temporal structure. That is while many problems in computer visionExpand
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Learning human activities and object affordances from RGB-D videos
Understanding human activities and object affordances are two very important skills, especially for personal robots which operate in human environments. In this work, we consider the problem ofExpand
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Unstructured human activity detection from RGBD images
Being able to detect and recognize human activities is essential for several applications, including personal assistive robotics. In this paper, we perform detection and recognition of unstructuredExpand
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Learning Depth from Single Monocular Images
We consider the task of depth estimation from a single monocular image. We take a supervised learning approach to this problem, in which we begin by collecting a training set of monocular images (ofExpand
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A dynamic ID-based remote user authentication scheme
Password-based authentication schemes are the most widely used techniques for remote user authentication. Many static ID-based remote user authentication schemes both with and without smart cardsExpand
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Human Activity Detection from RGBD Images
Being able to detect and recognize human activities is important for making personal assistant robots useful in performing assistive tasks. The challenge is to develop a system that is low-cost,Expand
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Robotic Grasping of Novel Objects using Vision
We consider the problem of grasping novel objects, specifically objects that are being seen for the first time through vision. Grasping a previously unknown object, one for which a 3-d model is notExpand
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3-D Depth Reconstruction from a Single Still Image
Abstract We consider the task of 3-d depth estimation from a single still image. We take a supervised learning approach to this problem, in which we begin by collecting a training set of monocularExpand
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Recurrent Neural Networks for driver activity anticipation via sensory-fusion architecture
Anticipating the future actions of a human is a widely studied problem in robotics that requires spatio-temporal reasoning. In this work we propose a deep learning approach for anticipation inExpand
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