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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...
In this work, we consider long-term topological place learning and present an approach that enables the robot to learn in an unsupervised, organized and incremental manner. The knowledge associated with the previously visited places is internally stored in the form of bubble descriptor semantic tree (BDST) using the previously proposed bubble space representation. The BDST is generated and maintained...
With the recent developments in sensor technology including Microsoft Kinect, it has now become much easier to augment visual data with three-dimensional depth information. In this paper, we propose a new approach to RGB-D based topological place representation—building on bubble space. While bubble space representation is in principle transparent to the type and number of sensory inputs employed,...
Place representation is a key element in topological maps. This paper presents bubble space - a novel representation for “places” (nodes) in topological maps. The novelties of this model are two-fold: First, a mathematical formalism that defines bubble space is presented. This formalism extends previously proposed bubble memory to accommodate two new variables - varying robot pose and multiple features...
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