Victoria Chen

Andre Cleaver, Darren Tang, Victoria Chen et al.

Due to the COVID-19 pandemic, conducting Human-Robot Interaction (HRI) studies in person is not permissible due to social distancing practices to limit the spread of the virus. Therefore, a virtual reality (VR) simulation with a virtual robot may offer an alternative to real-life HRI studies. Like a real intelligent robot, a virtual robot can utilize the same advanced algorithms to behave autonomously. This paper introduces HAVEN (HRI-based Augmentation in a Virtual robot Environment using uNity), a VR simulation that enables users to interact with a virtual robot. The goal of this system design is to enable researchers to conduct HRI Augmented Reality studies using a virtual robot without being in a real environment. This framework also introduces two common HRI experiment designs: a hallway passing scenario and human-robot team object retrieval scenario. Both reflect HAVEN's potential as a tool for future AR-based HRI studies.

Hadis Anahideh, Jay Rosenberger, Victoria Chen

Optimizing expensive black-box systems with limited data is an extremely challenging problem. As a resolution, we present a new surrogate optimization approach by addressing two gaps in prior research -- unimportant input variables and inefficient treatment of uncertainty associated with the black-box output. We first design a new flexible non-interpolating parsimonious surrogate model using a partitioning-based multivariate adaptive regression splines approach, Tree Knot MARS (TK-MARS). The proposed model is specifically designed for optimization by capturing the structure of the function, bending at near-optimal locations, and is capable of screening unimportant input variables. Furthermore, we develop a novel replication approach called \emph{Smart-Replication}, to overcome the uncertainty associated with the black-box output. The Smart-Replication approach identifies promising input points to replicate and avoids unnecessary evaluations of other data points. Smart-Replication is agnostic to the choice of a surrogate and can adapt itself to an unknown noise level. Finally to demonstrate the effectiveness of our proposed approaches we consider different complex global optimization test functions from the surrogate optimization literature. The results indicate that TK-MARS outperforms original MARS within a surrogate optimization algorithm and successfully detects important variables. The results also show that although non-interpolating surrogates can mitigate uncertainty, replication is still beneficial for optimizing highly complex black-box functions. The robustness and the quality of the final optimum solution found through Smart-Replication are competitive with that using no replications in environments with low levels of noise and using a fixed number of replications in highly noisy environments.