Classifying types of gesture and inferring intent

In order to infer intent from gesture, a rudimentary classification of types of gestures into five main classes is introduced. The classification is intended as a basis for incorporating the understanding of gesture into human-robot interaction (HRI). Some requirements for the operational classification of gesture by a robot interacting with humans are also suggested

Effect of Tactile Feedback on Performance

Humans interact with their environment by obtaining information from various modalities of sensing. These various modalities of sensing combine to facilitate manipulation and interaction with objects and the environment. The way humans interact with computers mirrors this environmental interaction with the absence of feedback from the tactile channel. The majority of computer operation is completed visually because currently, the primary feedback humans receive from computers is through the eyes. This strong dependence on the visual modality can cause visual fatigue and fixation on displays, resulting in errors and a decrease in performance. Distributing tasks and information across sensory modalities could possibly solve this problem. This study added tactile feedback to the human computer interface through vibration of a mouse to more accurately reflect a human\u27s multi-sensory interaction with their environment. This investigation used time off target to measure performance in a pursuit-tracking task. The independent variables were type of feedback with two levels, (i.e., tactile feedback vs no tactile feedback) and speed of target at three different levels, (i.e., slow, medium, and fast). Tactile feedback improved pursuit-tracking performance by 6%. Significant main effects where found for both the speed and feedback factors, but no significant interaction between speed and feedback was obtained. This improvement in performance was consistent with previous research, and lends further support to the advantages multimodal feedback may have to offer man-machine interfaces

Understanding the embodied teacher : nonverbal cues for sociable robot learning

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2008.Includes bibliographical references (p. 103-107).As robots enter the social environments of our workplaces and homes, it will be important for them to be able to learn from natural human teaching behavior. My research seeks to identify simple, non-verbal cues that human teachers naturally provide that are useful for directing the attention of robot learners. I conducted two novel studies that examined the use of embodied cues in human task learning and teaching behavior. These studies motivated the creation of a novel data-gathering system for capturing teaching and learning interactions at very high spatial and temporal resolutions. Through the studies, I observed a number of salient attention-direction cues, the most promising of which were visual perspective, action timing, and spatial scaffolding. In particular, this thesis argues that spatial scaffolding, in which teachers use their bodies to spatially structure the learning environment to direct the attention of the learner, is a highly valuable cue for robotic learning systems. I constructed a number of learning algorithms to evaluate the utility of the identified cues. I situated these learning algorithms within a large architecture for robot cognition, augmented with novel mechanisms for social attention and visual perspective taking. Finally, I evaluated the performance of these learning algorithms in comparison to human learning data, providing quantitative evidence for the utility of the identified cues. As a secondary contribution, this evaluation process supported the construction of a number of demonstrations of the humanoid robot Leonardo learning in novel ways from natural human teaching behavior.by Matthew Roberts Berlin.Ph.D

Computer simulation of a pilot in V/STOL aircraft control loops

The objective was to develop a computerized adaptive pilot model for the computer model of the research aircraft, the Harrier II AV-8B V/STOL with special emphasis on propulsion control. In fact, two versions of the adaptive pilot are given. The first, simply called the Adaptive Control Model (ACM) of a pilot includes a parameter estimation algorithm for the parameters of the aircraft and an adaption scheme based on the root locus of the poles of the pilot controlled aircraft. The second, called the Optimal Control Model of the pilot (OCM), includes an adaption algorithm and an optimal control algorithm. These computer simulations were developed as a part of the ongoing research program in pilot model simulation supported by NASA Lewis from April 1, 1985 to August 30, 1986 under NASA Grant NAG 3-606 and from September 1, 1986 through November 30, 1988 under NASA Grant NAG 3-729. Once installed, these pilot models permitted the computer simulation of the pilot model to close all of the control loops normally closed by a pilot actually manipulating the control variables. The current version of this has permitted a baseline comparison of various qualitative and quantitative performance indices for propulsion control, the control loops and the work load on the pilot. Actual data for an aircraft flown by a human pilot furnished by NASA was compared to the outputs furnished by the computerized pilot and found to be favorable

A study of manual control methodology with annotated bibliography

Manual control methodology - study with annotated bibliograph

Fuzzy logic application for modeling man-in-the-loop space shuttle proximity operations

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1994.Includes bibliographical references (p. 153-155).by Robert B. Brown.M.S

Human Factors Considerations in System Design

Human factors considerations in systems design was examined. Human factors in automated command and control, in the efficiency of the human computer interface and system effectiveness are outlined. The following topics are discussed: human factors aspects of control room design; design of interactive systems; human computer dialogue, interaction tasks and techniques; guidelines on ergonomic aspects of control rooms and highly automated environments; system engineering for control by humans; conceptual models of information processing; information display and interaction in real time environments

The Effect of Target Position and Tactual Recognition Field Size on Touch Bias and Accuracy

Past studies have shown that touchscreen display angles other than those that perpendicularly bisect the operator\u27s line of sight cause the operator to touch slightly below the target. The amount of touch bias created from this misjudgment fluctuates according to the target\u27s position on the screen. Additionally, the percentage of touches that activate a specific target varies according to the size of the tactual recognition field. Out of three square tactual recognition field sizes, this study sought to match these fields with the amount of touch bias occurring in each location (i.e., small amount of touch bias requires only a small field). The results showed that although bias differed according to location, the tactual recognition fields did not vary enough in size, nor were they large enough to find a significant difference between them in the number of touches captured according to the location of the target

NASA/NBS (National Aeronautics and Space Administration/National Bureau of Standards) standard reference model for telerobot control system architecture (NASREM)

The document describes the NASA Standard Reference Model (NASREM) Architecture for the Space Station Telerobot Control System. It defines the functional requirements and high level specifications of the control system for the NASA space Station document for the functional specification, and a guideline for the development of the control system architecture, of the 10C Flight Telerobot Servicer. The NASREM telerobot control system architecture defines a set of standard modules and interfaces which facilitates software design, development, validation, and test, and make possible the integration of telerobotics software from a wide variety of sources. Standard interfaces also provide the software hooks necessary to incrementally upgrade future Flight Telerobot Systems as new capabilities develop in computer science, robotics, and autonomous system control