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Task level programming based on skills has often been proposed as a mean to decrease programming complexity of industrial robots. Several models are based on encapsulating complex motions into self-contained primitive blocks. A semantic skill is then defined as a deterministic sequence of these primitives. A major limitation is that existing frameworks do not support the coordination of concurrentTask level programming based on skills has often been proposed as a mean to decrease programming complexity of industrial robots. Several models are based on encapsulating complex motions into self-contained primitive blocks. A semantic skill is then defined as a deterministic sequence of these primitives. A major limitation is that existing frameworks do not support the coordination of concurrent

In this paper we present a novel 3D sensing approach for industrial bin-picking applications that is low-cost, fast and precise. The system uses a simple laser-line emitter. While a robot arm moves the object through the laser light, a synchronized camera captures the laser line image on the object. A full point cloud as well as an edge point cloud suitable for subsequent pose estimation is genera

Despite the large scientific interest on robot learning forobject picking tasks, the research on object placingis too limited. Commonly, placing is simplistically consideredas a trivial task, but real life manipulation problems indicatethe exact opposite. A placing task can have different levels ofcomplexity, ranging from the simplest tabletop placing of anobject, to more complex cases such as loa

In certain cases analytical derivation of physics-based models of robots is difficult or even impossible. A potential workaround is the approximation of robot models from sensor data-streams employing machine learning approaches. In this paper, the inverse dynamics models are learned by employing a novel real-time deep learning algorithm. The algorithm exploits the methods of self-organized learni

In certain cases analytical derivation of physicsbased models of robots is difficult or even impossible. A potential workaround is the approximation of robot models fromsensor data-streams employing machine learning approaches.In this paper, the inverse dynamics models are learned byemploying a learning algorithm, introduced in [1], which isbased on reservoir computing in conjunction with self-org

The low automation level of industrial manufacturing processes, in conjunction with the limited research in the field of object manipulation for placing tasks, rise the need for adaptive and accurate industrial robotic systems that can manipulate a variety of objects in an uncertain environment. We deal with those issues by providing insights into two modules of a skilled-based architecture, the SThe low automation level of industrial manufacturing processes, in conjunction with the limited research in the field of object manipulation for placing tasks, rise the need for adaptive and accurate industrial robotic systems that can manipulate a variety of objects in an uncertain environment. We deal with those issues by providing insights into two modules of a skilled-based architecture, the S

In order for manufacturing companies to remain competitive while also offering a high degree of customization for the customers, flexible robots that can be rapidly reprogrammed to new tasks need to be applied in the factories. In this paper we propose a method for the intuitive programming of an industrial mobile robot by combining robot skills, a graphical user interface and human gesture recogn

Despite a lot of research in the field, only very little experience exists with Teaching by Demonstration (TbD) in actual industrial use cases. In the factory of the future, it is necessary to rapidly reprogram flexible mobile manipulators to perform new tasks, when the need arises, for which a working system capable of TbD would be ideal. Contrary to current TbD approaches, that generally aim to Despite a lot of research in the field, only very little experience exists with Teaching by Demonstration (TbD) in actual industrial use cases. In the factory of the future, it is necessary to rapidly reprogram flexible mobile manipulators to perform new tasks, when the need arises, for which a working system capable of TbD would be ideal. Contrary to current TbD approaches, that generally aim to

Automated task planning for robots is usually implemented on a motion primitive domain, where the focus is on constructing meaningful, general motion primitives. In this work we propose planning on the higher abstraction level of robot skills. In this context, skills are general, functional blocks that contain both sensing and action, have a welldefined expected outcome and set of preconditions, a

This book constitutes the refereed proceedings of the 10th International Conference on Computer Vision Systems, ICVS 2015, held in Copenhagen, Denmark, in July 2015. The 48 papers presented were carefully reviewed and selected from 92 submissions. The paper are organized in topical sections on biological and cognitive vision; hardware-implemented and real-time vision systems; high-level vision; le

Purpose– The purpose of this study has been to evaluate the technology of autonomous mobile manipulation in a real world industrial manufacturing environment. The objective has been to obtain experience in the integration with existing equipment and determine key challenges in maturing the technology to a level of readiness suitable for industry. Despite much research within the topic of industriaPurpose– The purpose of this study has been to evaluate the technology of autonomous mobile manipulation in a real world industrial manufacturing environment. The objective has been to obtain experience in the integration with existing equipment and determine key challenges in maturing the technology to a level of readiness suitable for industry. Despite much research within the topic of industria

Cognitive robots, able to adapt their actions based on sensory information and the management of uncertainty, have begun to find their way into manufacturing settings. However, the full potential of these robots has not been fully exploited, largely due to the lack of vertical integration with existing IT infrastructures, such as the manufacturing execution system (MES), as part of a large-scale cCognitive robots, able to adapt their actions based on sensory information and the management of uncertainty, have begun to find their way into manufacturing settings. However, the full potential of these robots has not been fully exploited, largely due to the lack of vertical integration with existing IT infrastructures, such as the manufacturing execution system (MES), as part of a large-scale c

This book summarizes various approaches for the automatic detection of health threats to older patients at home living alone. The text begins by briefly describing those who would most benefit from healthcare supervision. The book then summarizes possible scenarios for monitoring an older patient at home, deriving the common functional requirements for monitoring technology. Next, the work identif

The automation of kitting tasks-collecting a set of parts for one particular car into a kit-has a huge impact in the automotive industry. It considerably increases the automation levels of tasks typically conducted by human workers. Collecting the parts involves picking up objects from pallets and bins as well as placing them in the respective compartments of the kitting box. In this paper, we pre

Delegating tasks from a human to a robot needs an efficient and easy-to-use communication pipeline between them - especially when inexperienced users are involved. This work presents a robotic system that is able to bridge this communication gap by exploiting 3D sensing for gesture recognition and real-time object segmentation. We visually extract an unknown object indicated by a human through a pDelegating tasks from a human to a robot needs an efficient and easy-to-use communication pipeline between them - especially when inexperienced users are involved. This work presents a robotic system that is able to bridge this communication gap by exploiting 3D sensing for gesture recognition and real-time object segmentation. We visually extract an unknown object indicated by a human through a p

3D pose estimation is a crucial element for enabling robots to work in industrial environment to perform tasks like bin-picking or depalletizing. Even though there exist various pose estimation algorithms, they usually deal with common daily objects applied in lab environments. However, coping with real-world industrial objects is a much harder challenge for most pose estimation techniques due to 3D pose estimation is a crucial element for enabling robots to work in industrial environment to perform tasks like bin-picking or depalletizing. Even though there exist various pose estimation algorithms, they usually deal with common daily objects applied in lab environments. However, coping with real-world industrial objects is a much harder challenge for most pose estimation techniques due to

The recent development of calibration algorithms has been driven into two major directions: (1) an increasing accuracy of mathematical approaches and (2) an increasing flexibility in usage by reducing the dependency on calibration objects. These two trends, however, seem to be contradictory since the overall accuracy is directly related to the accuracy of the pose estimation of the calibration obj

The registration of an observed point set to a known model to estimate its 3D pose is a common task for the autonomous manipulation of objects. Especially in industrial environments, robotic systems need to accurately estimate the pose of objects in order to successfully perform picking, placing or assembly tasks. However, the characteristics of industrial objects often cause difficulties for clas