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Manipulation tasks requiring high precision are difficult for reasons such as imprecise calibration and perceptual inaccuracies. We present a method for visual task outcome verification that provides an assessment of the task status as well as information for the robot to improve this status. The final status of the task is assessed as success, failure or in progress. We propose a deep learning strategy...
Identifying an object of interest, grasping it, and handing it over are key capabilities of collaborative robots. In this context we propose a fast, supervised learning framework for learning associations between human hand gestures and the intended robotic manipulation actions. This framework enables the robot to learn associations on the fly while performing a task with the user. We consider a domestic...
As human-robot collaboration methodologies develop robots need to adapt fast learning methods in domestic scenarios. The paper presents a novel approach to learn associations between the human hand gestures and the robot’s manipulation actions. The role of the robot is to operate as an assistant to the user. In this context we propose a supervised learning framework to explore the gesture-action space...
Understanding semantic meaning from hand gestures is a challenging but essential task in human-robot interaction scenarios. In this paper we present a baseline evaluation of the Innsbruck Multi-View Hand Gesture (IMHG) dataset [1] recorded with two RGB-D cameras (Kinect). As a baseline, we adopt a probabilistic appearance-based framework [2] to detect a hand gesture and estimate its pose using two...
Deictic gestures - pointing at things in human-human collaborative tasks - constitute a pervasive, non-verbal way of communication, used e.g. to direct attention towards objects of interest. In a human-robot interactive scenario, in order to delegate tasks from a human to a robot, one of the key requirements is to recognize and estimate the pose of the pointing gesture. Standard approaches rely on...
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