We are interested in machines that can learn new things from people who are not Machine Learning (ML) experts. We propose a research agenda framed around the human factors (HF) and ML research questions of teaching an agent via demonstration and critique. Ultimately, we will develop a training simulation game with several nonplayer characters, all of which can be easily taught new behaviors by an end-user.
Decision makers are consistently asked to make decisions about the course of action required to achieve mission success regardless of the time pressure and the quantity and quality of information available. To be successful, they will adapt their decision strategies to the environment and even use heuristics, simple rules that use little information and can be processed quickly. To support these decision makers, we are designing proactive decision support systems that support adaptive decision making along a range analytic and heuristic strategies.
Support improved decision making under high stress, uncertain operational conditions through the development of proactive, context-based decision support aids. The objective of this project is to create a scientifically-principled design specification and prototype concepts for a set of decision aids capable of supporting decision making and judgment across multi-faceted mission with dynamic tasking requirements.
NextGen systems are envisioned to be composed of human and automated agents interacting with dynamic flexibility in the allocation of authority and autonomy. The analysis of such concepts of operation requires methods for verifying and validating that the range of roles and responsibilities potentially assignable to the human and automated agents does not lead to unsafe situations.
PURPOSE: The intent of this research is to (1) understand the gap between what is needed in industry to respond to this issue and what is taught in the aerospace engineering design curriculum and (2) design and implement educational interventions to address this gap and improve the ability of students to take into account stakeholder requirements.
NSF NRI-Small: Understanding Neuromuscular Adaptations in Human-Robot Physical Interaction for Adaptive Robot Co-Workers
The goal of this award is to develop theories, methods, and tools to understand the mechanisms of neuromotor adaptation in human-robot physical interaction. Human power-assisting systems, e.g., powered lifting devices that aid human operators in manipulating heavy or bulky loads, require physical contact between the operator and machine, creating a coupled dynamic system.