Virtual platforms provide benefits to developers in terms of a more rapid development cycle since development may begin before next-generation hardware is available. However, there is a distinct lack of graphics virtualization in industry-grade virtual platforms, leading to performance issues that may reduce the benefits virtual platforms otherwise have over execution on actual hardware. This paper demonstrates graphics acceleration by the means of paravirtualizing OpenGL ES in the Wind River Simics full-system simulator. We propose a solution for paravirtualized graphics using magic instructions to share memory between target and host systems, and present an implementation utilizing this method. The study illustrates the benefits and drawbacks of paravirtualized graphics acceleration and presents a performance analysis of strengths and weaknesses compared to software rasterization. Additionally, benchmarks are devised to stress key aspects in the solution, such as communication latency and computationally intensive applications. We assess paravirtualization as a viable method to accelerate graphics in system simulators, this reduces frame times up to 34 times compared to that of software rasterization. Furthermore, magic instructions are identified as the primary bottleneck of communication latency in the implementation.

Acquiring, representing and modeling human skills is one of the key research areas in teleoperation, programming-by-demonstration and human-machine collaborative settings. One of the common approaches is to divide the task that the operator is executing into several subtask in order to provide manageable modeling. In this paper we consider the use of a layered hidden Markov model (LHMM) to model human skills. We evaluate a gestem classifier that classifies motions into basic action-primitives, or gestems. The gestem classifiers are then used in a LHMM to model a simulated teleoperated task. We investigate the online and offline classification performance with respect to noise, number of gestems, type of HMM and the available number of training sequences. We also apply the LHMM to data recorded during the execution of a trajectory-tracking task in 2D and 3D with a robotic manipulator in order to give qualitative as well as quantitative results for the proposed approach. The results indicate that the LHMM is suitable for modeling teleoperative trajectory-tracking tasks and that the difference in classification performance between one and multi-dimensional HMMs for gestem classification are small. It can also be seen that the LHMM is robust w.r.t misclassifications in the underlying gestem classifiers

The next generation of robots will have to learn new tasks or refine the existing ones through direct interaction with the environment or through a teaching/coaching process in programming by demonstration (PbD) and learning by instruction frameworks. In this paper, we propose to extend the classical PbD approach with a graphical language that makes robot coaching easier. The main idea is based on graphical programming where the user designs complex robot tasks by using a set of low-level action primitives. Different to other systems, our action primitives are made general and flexible so that the user can train them online and therefore easily design high level tasks

In a service robot scenario, we are interested in a task of building maps of the environment that include automatically recognized objects. Most systems for simultaneous localization and mapping (SLAM) build maps that are only used for localizing the robot. Such maps are typically based on grids or different types of features such as point and lines. Here, we augment the process with an object recognition system that detects objects in the environment and puts them in the map generated by the SLAM system. During task execution, the robot can use this information to reason about objects, places and their relationships. The metric map is also split into topological entities corresponding to rooms. In this way, the user can command the robot to retrieve an object from a particular room or get help from a robot when searching for a certain object

Segmentation and recognition of operator-generated motions are commonly facilitated to provide appropriate assistance during task execution in teleoperative and human-machine collaborative settings. The assistance is usually provided in a virtual fixture framework where the level of compliance can be altered online, thus improving the performance in terms of execution time and overall precision. However, the fixtures are typically inflexible, resulting in a degraded performance in cases of unexpected obstacles or incorrect fixture models. In this paper, we present a method for online task tracking and propose the use of adaptive virtual fixtures that can cope with the above problems. Here, rather than executing a predefined plan, the operator has the ability to avoid unforeseen obstacles and deviate from the model. To allow this, the probability of following a certain trajectory (subtask) is estimated and used to automatically adjusts the compliance, thus providing the online decision of how to fixture the movement

It has been demonstrated in a number of robotic areas how the use of virtual fixtures improves task performance both in terms of execution time and overall precision, [1]. However, the fixtures are typically inflexible, resulting in a degraded performance in cases of unexpected obstacles or incorrect fixture models. In this paper, we propose the use of adaptive virtual fixtures that enable us to cope with the above problems. A teleoperative or human machine collaborative setting is assumed with the core idea of dividing the task, that the operator is executing, into several subtasks. The operator may remain in each of these subtasks as long as necessary and switch freely between them. Hence, rather than executing a predefined plan, the operator has the ability to avoid unforeseen obstacles and deviate from the model. In our system, the probability that the user is following a certain trajectory (subtask) is estimated and used to automatically adjusts the compliance. Thus, an on-line decision of how to fixture the movement is provided.

It has been demonstrated in a number of robotic areas how the use of virtual fixtures improves task performance both in terms of execution time and overall precision, [1]. However, the fixtures are typically inflexible, resulting in a degraded performance in cases of unexpected obstacles or incorrect fixture models. In this paper, we propose the use of adaptive virtual fixtures that enable us to cope with the above problems. A teleoperative or human machine collaborative setting is assumed with the core idea of dividing the task, that the operator is executing, into several subtasks. The operator may remain in each of these subtasks as long as necessary and switch freely between them. Hence, rather than executing a predefined plan, the operator has the ability to avoid unforeseen obstacles and deviate from the model. In our system, the probability that the user is following a certain trajectory (subtask) is estimated and used to automatically adjusts the compliance. Thus, an on-line decision of how to fixture the movement is provided.

Probabilistic roadmap methods (PRM) have been successfully used to solve difficult path planning problems but their efficiency is limited when the free space contains narrow passages through which the robot must pass. This paper presents a new sampling scheme that aims to increase the probability of finding paths through narrow passages. Here, a biased sampling scheme is used to increase the distribution of nodes in narrow regions of the free space. A partial computation of the artificial potential field is used to bias the distribution of nodes.