We present a novel methodology for recovering meteorite falls observed and constrained by fireball networks, using drones and machine learning algorithms. This approach uses images of the local terrain for a given fall site to train an artificial neural network, designed to detect meteorite candidates. We have field tested our methodology to show a meteorite detection rate between 75% and 97%, while also providing an efficient mechanism to eliminate false positives. Our tests at a number of locations within Western Australia also showcase the ability for this training scheme to generalize a model to learn localized terrain features. Our model training approach was also able to correctly identify three meteorites in their native fall sites that were found using traditional searching techniques. Our methodology will be used to recover meteorite falls in a wide range of locations within globe-spanning fireball networks.

This study explores employing a measurement of implicit attitudes to better understand attitudes and trust levels towards robots. This work builds upon an existing implicit measure (Implicit Associations Test) to compare attitudes toward humans with attitudes toward robots. Results are compared with explicit self-report measures, and future directions for this work are discussed.

In this work we investigate the effects of robot appearance and reliability on a user’s trust levels through an experiment where participants reacted to three different robot forms that either behaved reliably or unreliably during a series of experimental trials. A final trial was implemented to evaluate use choice by allowing participants to choose their preferred robot and complete an additional trial with that robot. Results from this pilot experimentation indicated differences based on the reliability of the robot, as well as whether the participant chose to interact with the robot.

Virtual reality is becoming increasingly popular in today’s society. With this proliferation it becomes even more important to study the effects such environments may have on one’s perception of reality. Two pilot studies were run in order to provide insight into the relationship between time perception and flow in a virtual environment. In Experiment 1 participants played a music-oriented virtual game for 2 minutes. In Experiment 2 participants played a space-shooter virtual game for 5 minutes. Duration Judgment Ratio (DJR) and Flow State Scale (FSS-2) measures were taken and compared to one another. Though a relationship between DJR and Flow was not found in each experiment individually, insights gained from the comparison of the two experiments may provide additional understandings. The results of this pilot study could aid researchers in developing objective ways to measure components of flow especially with respect to virtual environments. Additional insights and applications are discussed.

This experiment explored the influence of users’ experience (prior interaction) with robots on their attitudes and trust toward robotic agents. Specifically, we hypothesized that prior experience would lead to 1) higher trust scores after viewing a robot complete a task, 2) smaller differences in trust scores when comparing a human and a robot completing the same task, and 3) more positive general attitudes towards robots. These hypotheses were supported although not all results achieved significant levels of differentiation. These findings confirm that prior experience plays an important role in both user trust and general attitude in human-robot interactions.