Task-level control for autonomous robots

Task-level control refers to the integration and coordination of planning, perception, and real-time control to achieve given high-level goals. Autonomous mobile robots need task-level control to effectively achieve complex tasks in uncertain, dynamic environments. This paper describes the Task Control Architecture (TCA), an implemented system that provides commonly needed constructs for task-level control. Facilities provided by TCA include distributed communication, task decomposition and sequencing, resource management, monitoring and exception handling. TCA supports a design methodology in which robot systems are developed incrementally, starting first with deliberative plans that work in nominal situations, and then layering them with reactive behaviors that monitor plan execution and handle exceptions. To further support this approach, design and analysis tools are under development to provide ways of graphically viewing the system and validating its behavior

Specialized data analysis for the Space Shuttle Main Engine and diagnostic evaluation of advanced propulsion system components

The Marshall Space Flight Center is responsible for the development and management of advanced launch vehicle propulsion systems, including the Space Shuttle Main Engine (SSME), which is presently operational, and the Space Transportation Main Engine (STME) under development. The SSME's provide high performance within stringent constraints on size, weight, and reliability. Based on operational experience, continuous design improvement is in progress to enhance system durability and reliability. Specialized data analysis and interpretation is required in support of SSME and advanced propulsion system diagnostic evaluations. Comprehensive evaluation of the dynamic measurements obtained from test and flight operations is necessary to provide timely assessment of the vibrational characteristics indicating the operational status of turbomachinery and other critical engine components. Efficient performance of this effort is critical due to the significant impact of dynamic evaluation results on ground test and launch schedules, and requires direct familiarity with SSME and derivative systems, test data acquisition, and diagnostic software. Detailed analysis and evaluation of dynamic measurements obtained during SSME and advanced system ground test and flight operations was performed including analytical/statistical assessment of component dynamic behavior, and the development and implementation of analytical/statistical models to efficiently define nominal component dynamic characteristics, detect anomalous behavior, and assess machinery operational condition. In addition, the SSME and J-2 data will be applied to develop vibroacoustic environments for advanced propulsion system components, as required. This study will provide timely assessment of engine component operational status, identify probable causes of malfunction, and indicate feasible engineering solutions. This contract will be performed through accomplishment of negotiated task orders

Modeling human visual behavior in dynamic 360º environments.

Virtual reality (VR) is rapidly growing: Advances in hardware, together with the current high computational power, are driving this technology, which has the potential to change the way people consume content, and has been predicted to become the next big computing paradigm. However, although it has become accessible at a consumer level, much still remains unknown about the grammar and visual language in this medium. Understanding and predicting how humans behave in virtual environments remains an open problem, since the visual behavior known for traditional screen-based content does not hold for immersive VR environments: In VR, the user has total control of the camera, and therefore content creators cannot ensure where viewers’ attention will be directed to. This understanding of visual behavior, however, can be crucial in many applications, such as novel compression and rendering techniques, content design, or virtual tourism, among others. Some works have been devoted to analyzing and modeling human visual behavior. Most of them have focused on identifying the content’s regions that attract the observers’ visual attention, resorting to saliency as a topological measure of what part of a virtual scene might be of more interest. When consuming virtual reality content, which can be either static (i.e., 360◦ images) or dynamic (i.e., 360◦ videos), there are many factors that affect human visual behavior, which are mainly associated with the scene shown in the VR video or image (e.g., colors, shapes, movements, etc.), but also depend on the subjects observing it (their mood and background, the task being performed, previous knowledge, etc.). Therefore, all these variables affecting saliency make its prediction a challenging task. This master thesis presents a novel saliency prediction model for VR videos based on a deep learning approach (DL). DL networks have shown outstanding results in image processing tasks, automatically inferring the most relevant information from images. The proposed model is the first to exploit the joint potential of convolutional (CNN) and recurrent (RNN) neural networks to extract and model the inherent spatio-temporal features from videos, employing RNNs to account for temporal information at the time of feature extraction, rather than to post-process spatial features as in previous works. It is also tailored to the particularities of dynamic VR videos, with the use of spherical convolutions and a novel spherical loss function for saliency prediction that work on a 3D space rather than in traditional image space. To facilitate spatio-temporal learning, this work is also the first in including the optical flow between 360◦ frames for saliency prediction, since movement is known to be a highly salient feature in dynamic content. The proposed model was evaluated qualitatively and quantitatively, proving to outperform state-of-the-art works. Moreover, an exhaustive ablation study demonstrates the effectiveness of the different design decisions made throughout the development of the model. <br /

Cue utilization and strategy application in stable and unstable dynamic environments

http://dx.doi.org/10.1016/j.cogsys.2010.12.004 Copyright © 2011, Elsevie

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

Speeding up Learning with Dynamic Environment Shaping in Evolutionary Robotics

Evolutionary Robotics is a promising approach to automatically build efficient controllers using stochastic optimization techniques. However, works in this area are often confronted to complex environments where even simple tasks cannot be achieved. In the scope of this paper, we propose an approach based on explicit problem decomposition and dynamic environment shaping to ease the learning task