The Interplay of Spatial Ability, Sex, Training Modality, and Environmental Features: Effects on Spatial Cognition, Mental Map Formation, and Wayfinding

This research examined the processes involved when one is acquiring spatial knowledge while traversing an environment, integrating that knowledge into mental representations, and subsequently relying on that knowledge to successfully perform wayfinding. Particularly, the conducted studies aimed to provide evidence reflecting on two ongoing debates: the distinctions between types of knowledge embedded in mental maps, and the unconfirmed sequential or simultaneous nature of the acquisition and integration of those types. The experiments reported in this manuscript addressed drawbacks in existing research by manipulating opportunities for the acquisition of point and route knowledge (two of the potentially distinct knowledge types), and by testing participants\u27 capacity to integrate their acquired knowledge in the context of environmental affordances. Participants in the conducted studies underwent environmental training exposures targeted at providing a) primarily point knowledge or b) route knowledge acquisition, and they also completed a set of knowledge measures tapping point, route, and configuration knowledge. Finally, participants completed tests of wayfinding capacity to demonstrate their ability to rely on integrated mental maps for successful wayfinding. Results of the two conducted studies provided substantial evidence that there are distinct types of knowledge that may be acquired and quantifiably measured, and that spatial knowledge can be acquired in parallel, not necessarily in sequence, across knowledge types. Critically, some knowledge types may also develop in exclusion to others especially for individuals with particular spatial abilities and predispositions. Accordingly, it is likely that previous research indicating sequential spatial knowledge development may be reflecting differential acquisition as a combination of population capabilities and knowledge measurement research methodologies. Finally, the results demonstrate that individual differences, training modalities, and environment iv features have complex, interacting effects on spatial cognition and that no one factor predominantly determines individuals\u27 ability to acquire, integrate, or employ spatial knowledge

Development Of A Signal Detection-Based Task For Research On Distributed Human-Robot Teaming Within Immersive Virtual Reality

The purpose of this paper is to describe the design and validation of a dynamic signal detection-based task experienced within immersive virtual reality (IVR). The task will be used as a primary task for investigating the workload introduced by interface devices used for human-robot team communications. Participants play the role of a Soldier performing a Cordon and Search operation monitoring an environment for threats. The task differs from traditional signal detection tasks in that it is continuous, dynamic, and signal stimuli move through participants\u27 field of view. Implementation of the task within simulation allows for direct control, measurement, and manipulation of multiple parameters that influence performance metrics, task difficulty, and participant workload. Lessons learned during the design and development of the task are shared to guide other researchers intending to implement a signal detection-based task within IVR

University of Central Florida ASIST Study 3 Results Registration

This registration contains the analyses and results following on from UCF's ASIST Study 3 Hypotheses preregistration

Evaluation And Benefits Of Head-Mounted Display Systems For Hri Research

The intent of this evaluation is to describe the unique benefits that may be provided to human robot interaction (HRI) researchers by the capabilities of commercially available binocular head-mounted displays (HMDs) and associated handheld controllers. Three popular HMDs (Oculus Rift, HTC Vive, and Google Daydream) were compared across eight factors: cost, head tracking fidelity, visual resolution, user mobility, hand tracking fidelity, number of input modes, adaptability of input, and provided tracking space. Each of these elements was considered in the context of their relevance to the field of HRI, and potential importance for conducting research in immersive virtual reality (IVR). A Pugh chart was developed to succinctly compare the pros and cons of each headset alongside a description of IVR tasks for HRI military research as well as examples taken from work currently being conducted in our lab

An Intelligence Architecture for Grounded Language Communication with Field Robots

For humans and robots to collaborate effectively as teammates in unstructured environments, robots must be able to construct semantically rich models of the environment, communicate efficiently with teammates, and perform sequences of tasks robustly with minimal human intervention, as direct human guidance may be infrequent and/or intermittent. Contemporary architectures for human-robot interaction often rely on engineered human-interface devices or structured languages that require extensive prior training and inherently limit the kinds of information that humans and robots can communicate. Natural language, particularly when situated with a visual representation of the robot’s environment, allows humans and robots to exchange information about abstract goals, specific actions, and/or properties of the environment quickly and effectively. In addition, it serves as a mechanism to resolve inconsistencies in the mental models of the environment across the human-robot team. This article details a novel intelligence architecture that exploits a centralized representation of the environment to perform complex tasks in unstructured environments. The centralized environment model is informed by a visual perception pipeline, declarative knowledge, deliberate interactive estimation, and a multimodal interface. The language pipeline also exploits proactive symbol grounding to resolve uncertainty in ambiguous statements through inverse semantics. A series of experiments on three different, unmanned ground vehicles demonstrates the utility of this architecture through its robust ability to perform language-guided spatial navigation, mobile manipulation, and bidirectional communication with human operators. Experimental results give examples of component-level behaviors and overall system performance that guide a discussion on observed performance and opportunities for future innovation.</jats:p