NV NASA EPSCoR Highlight: David Feil-Seifer, UNR

Nevada NASA EPSCoR Highlight

For socially assistive robots (SAR) to be accepted into complex and stochastic human environments, it is important to account for subtle social norms. For this project, we proposed a novel approach to socially aware navigation (SAN), which garnered significant interest from the Human-Robot Interaction (HRI) community. We used a multi-objective optimization tool called the Pareto Concavity Elimination Transformation (PaCcET) to capture nonlinear human navigation behavior, which is a novel contribution to the community. We used autonomously sensed, distance-based features that capture the social norms and associated social costs of a given trajectory point toward the goal. Rather than use a finely tuned linear combination of these costs, we used PaCcET to select an optimized future trajectory point associated with a nonlinear combination of the costs. Existing research in this domain concentrates on geometric reasoning and model-based learning approaches that have their own pros and cons.

Our approach is distinct from prior work in this area. In a simulation, we showed that the PaCcET-based trajectory planner not only can avoid collisions and reach the intended destination in static and dynamic environments, but it also considers a human’s personal space in the trajectory selection process.

Return on Investment (ROI) To Date

Proposals Funded: 1 ($500,000)
Peer-reviewed publications: 1
Presentations: 1
Students impacted, research experience and expertise: 6

NASA Content and Resources Used:
This research was funded by a Nevada NASA EPSCoR Research Infrastructure Development Seed Grant sub-award #NNX15AK48A.