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This paper presents a grasping system for picking up an indicated object in a complex real-world environment using a parallel jaw gripper. The proposed grasping scheme comprises the following three main steps: (1) A user indicates a target object and provides the system with a task instruction on how to grasp it, (2) the system acquires geometric information about the target object and constructs...
For successful deployment, personal robots must adapt to ever-changing indoor environments. While dealing with novel objects is a largely unsolved challenge in AI, it is easy for people. In this paper we present a framework for robot supervision through Amazon Mechanical Turk. Unlike traditional models of teleoperation, people provide semantic information about the world and subjective judgements...
Humanoid robotic assistants need capable and comprehensive perception systems that enable them to perform complex manipulation and grasping tasks. This requires the identification and recognition of supporting planes and objects in the world, together with their precise 6D poses. In this paper, we propose a 3D perception system architecture that can robustly fit CAD models in cluttered table setting...
This paper addresses the problem of grasp planning of unmodelled 3D objects with the aide of vision. The approach proposes the extension of fast 2D contour-based grasp planning algorithms, and the use of images of the target object from different points of view to recover critical 3D information like the size, location and the pose. A complete method is described, and experimental results are presented...
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