Here we embrace the concept of ethologically inspired Blender models which allow for the study of synergistic teamings between humans and animals focused toward achieving a specified objective.
Current approaches to automation generally do not consider the human operator’s role as a critical component of the autonomous system. Even though manufacturers tout the accuracy and safety of their respective autonomous systems, experience has proven otherwise as deaths in vehicles, and for pedestrians have occurred. These approaches generally do not consider the human operator’s role as a critical component of the autonomous system. Ultimately the collaborative research pursued here will help to avoid anticipated breakdowns in human performance and situational awareness during Human Robot Teaming operations such as in autonomous vehicles.
Our goal is to use the understanding of teamings between humans and animals, along with that of human cognitive models to improve human machine teaming for efficient automation.
More Specifically, we hope to use our collaborations to develop an engineering approach to cognition that provides a rigorous robust structure to determining human cognitive states. Additionaly, we strive to develop and apply tools and techniques using a state based approach that allows researchers to design in a well understood mathematical structure using readily available tools and techniques. This will result in an ability to determine the mental state of human operator for improved situational awareness will also allow more efficient “teaming” between machines and humans to successfully meet system mission goals.
Our community consist of experts from many different disciplines in an effort to better understand human cognition and its role in blended automation. Our collaborators include engineers, psychologist, neuroscientist, medical doctors and mathematicians working toward a common goal.
The new tools and techniques that will result from our combined collaborative efforts will be widely applicable to a broad class of cognition, automation and artificial intelligent systems.
Human cognition refers to the process of thinking. It is the identification of knowledge, of understanding it and perceiving it. Here it is specifically concerned with the internal mental processes that involve as a result of external stimulii. Our research focuses on the monitoring of human cognition to avoid anticipated breakdowns in situational awareness and human performance during autonomous driving.
The concept of ethologically inspired models for perception, control and automation allows us to study and understand the behavior of animals with an aim to develop systems that mimic such models for efficient autonomous behaviors. In our research we analyze the horse as the first “biological robot”, and explore the idea of the autonomous robot as a mechanical or electronic agent that can extend human capacities.
Here Complemation is the development of technology that is designed to aid in complementing human skills and abilities with automation rather than replacing them –”complemation” (Schutte, 1999).
Complemation aims to enhance human cognition in part by using covet brain and body signals to prevent cognitive and attention lapses in Human Machine Teaming operations.
We invite your participation in the STABLE. We have projects of interest in multiple domains seeking to provide technical solutions for the benefit of society. We work as a team and grow as a team sharing knowledge, inspiration and motivation for our joint success.Our projects and approaches are unconventional and provide unique challenges that inform unorthodox approaches.