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- Title
- ENHANCING SITUATIONAL AWARENESS THROUGH HAPTICS INTERACTION IN VIRTUAL ENVIRONMENT TRAINING SYSTMES.
- Creator
-
Hale, Kelly, Stanney, Kay, University of Central Florida
- Abstract / Description
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Virtual environment (VE) technology offers a viable training option for developing knowledge, skills and attitudes (KSA) within domains that have limited live training opportunities due to personnel safety and cost (e.g., live fire exercises). However, to ensure these VE training systems provide effective training and transfer, designers of such systems must ensure that training goals and objectives are clearly defined and VEs are designed to support development of KSAs required. Perhaps the...
Show moreVirtual environment (VE) technology offers a viable training option for developing knowledge, skills and attitudes (KSA) within domains that have limited live training opportunities due to personnel safety and cost (e.g., live fire exercises). However, to ensure these VE training systems provide effective training and transfer, designers of such systems must ensure that training goals and objectives are clearly defined and VEs are designed to support development of KSAs required. Perhaps the greatest benefit of VE training is its ability to provide a multimodal training experience, where trainees can see, hear and feel their surrounding environment, thus engaging them in training scenarios to further their expertise. This work focused on enhancing situation awareness (SA) within a training VE through appropriate use of multimodal cues. The Multimodal Optimization of Situation Awareness (MOSA) model was developed to identify theoretical benefits of various environmental and individual multimodal cues on SA components. Specific focus was on benefits associated with adding cues that activated the haptic system (i.e., kinesthetic/cutaneous sensory systems) or vestibular system in a VE. An empirical study was completed to evaluate the effectiveness of adding two independent spatialized tactile cues to a Military Operations on Urbanized Terrain (MOUT) VE training system, and how head tracking (i.e., addition of rotational vestibular cues) impacted spatial awareness and performance when tactile cues were added during training. Results showed tactile cues enhanced spatial awareness and performance during both repeated training and within a transfer environment, yet there were costs associated with including two cues together during training, as each cue focused attention on a different aspect of the global task. In addition, the results suggest that spatial awareness benefits from a single point indicator (i.e., spatialized tactile cues) may be impacted by interaction mode, as performance benefits were seen when tactile cues were paired with head tracking. Future research should further examine theoretical benefits outlined in the MOSA model, and further validate that benefits can be realized through appropriate activation of multimodal cues for targeted training objectives during training, near transfer and far transfer (i.e., real world performance).
Show less - Date Issued
- 2006
- Identifier
- CFE0001414, ucf:47034
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0001414
- Title
- FALCONET: FORCE-FEEDBACK APPROACH FOR LEARNING FROM COACHING AND OBSERVATION USING NATURAL AND EXPERIENTIAL TRAINING.
- Creator
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Stein, Gary, Gonzalez, Avelino, University of Central Florida
- Abstract / Description
-
Building an intelligent agent model from scratch is a difficult task. Thus, it would be preferable to have an automated process perform this task. There have been many manual and automatic techniques, however, each of these has various issues with obtaining, organizing, or making use of the data. Additionally, it can be difficult to get perfect data or, once the data is obtained, impractical to get a human subject to explain why some action was performed. Because of these problems, machine...
Show moreBuilding an intelligent agent model from scratch is a difficult task. Thus, it would be preferable to have an automated process perform this task. There have been many manual and automatic techniques, however, each of these has various issues with obtaining, organizing, or making use of the data. Additionally, it can be difficult to get perfect data or, once the data is obtained, impractical to get a human subject to explain why some action was performed. Because of these problems, machine learning from observation emerged to produce agent models based on observational data. Learning from observation uses unobtrusive and purely observable information to construct an agent that behaves similarly to the observed human. Typically, an observational system builds an agent only based on prerecorded observations. This type of system works well with respect to agent creation, but lacks the ability to be trained and updated on-line. To overcome these deficiencies, the proposed system works by adding an augmented force-feedback system of training that senses the agents intentions haptically. Furthermore, because not all possible situations can be observed or directly trained, a third stage of learning from practice is added for the agent to gain additional knowledge for a particular mission. These stages of learning mimic the natural way a human might learn a task by first watching the task being performed, then being coached to improve, and finally practicing to self improve. The hypothesis is that a system that is initially trained using human recorded data (Observational), then tuned and adjusted using force-feedback (Instructional), and then allowed to perform the task in different situations (Experiential) will be better than any individual step or combination of steps.
Show less - Date Issued
- 2009
- Identifier
- CFE0002746, ucf:48157
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0002746
