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In this paper, we present a system for vision-based grasp recognition, mapping and execution on a humanoid robot to provide an intuitive and natural communication channel between humans and humanoids. This channel enables a human user to teach a robot how to grasp an object. The system comprises three components: human upper body motion capture system which provides the approaching direction towards...
Markerless, vision based estimation of human hand pose over time is a prerequisite for a number of robotics applications, such as learning by demonstration (LbD), health monitoring, teleoperation, human-robot interaction. It has special interest in humanoid platforms, where the number of degrees of freedom makes conventional programming challenging. Our primary application is LbD in natural environments...
We study the problem of human to robot grasp mapping as a basic building block of a learning by imitation system. The human hand posture, including both the grasp type and hand orientation, is first classified based on a single image and mapped to a specific robot hand. A metric for the evaluation based on the notion of virtual fingers is proposed. The first part of the experimental evaluation, performed...
This paper presents a vision based method for grasp classification. It is developed as part of a Programming by Demonstration (PbD) system for which recognition of objects and pick-and-place actions represent basic building blocks for task learning. In contrary to earlier approaches, no articulated 3D reconstruction of the hand over time is taking place. The indata consists of a single image of the...
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