Learn More
Behavioral Targeting (BT) is a technique used by online advertisers to increase the effectiveness of their campaigns, and is playing an increasingly important role in the online advertising market. However, it is underexplored in academia when looking at how much BT can truly help online advertising in commercial search engines. To answer this question, in(More)
— Given an image and an aligned depth map of an object, our goal is to estimate the full 7-dimensional gripper configuration—its 3D location, 3D orientation and the gripper opening width. Recently, learning algorithms have been successfully applied to grasp novel objects—ones not seen by the robot before. While these approaches use low-dimensional(More)
As the World Wide Web in China grows rapidly, mining knowledge in Chinese Web pages becomes more and more important. Mining Web information usually relies on the machine learning techniques which require a large amount of labeled data to train credible models. Although the number of Chinese Web pages increases quite fast, it still lacks Chinese labeled(More)
For scene understanding, one popular approach has been to model the object-object relationships. In this paper, we hypothesize that such relationships are only an artifact of certain hidden factors, such as humans. For example, the objects, monitor and keyboard, are strongly spatially correlated only because a human types on the keyboard while watching the(More)
In this paper, we propose a method for grasping unknown objects from piles or cluttered scenes, given a point cloud from a single depth camera. We introduce a shape-based method − Symmetry Height Accumulated Features (SHAF) − that reduces the scene description complexity such that the use of machine learning techniques becomes feasible. We describe the(More)
—Placing is a necessary skill for a personal robot to have in order to perform tasks such as arranging objects in a disorganized room. The object placements should not only be stable but also be in their semantically preferred placing areas and orientations. This is challenging because an environment can have a large variety of objects and placing areas(More)
We consider the problem of learning object arrangements in a 3D scene. The key idea here is to learn how objects relate to human poses based on their affordances, ease of use and reachability. In contrast to mod-eling object-object relationships, modeling human-object relationships scales linearly in the number of objects. We design appropriate density(More)
While a significant body of work has been done on grasping objects, there is little prior work on placing and arranging objects in the environment. In this work, we consider placing multiple objects in complex placing areas, where neither the object nor the placing area may have been seen by the robot before. Specifically, the placements should not only be(More)