This research aims to demonstrate how integrating human-robot teaming dynamics into mission planning tools impacts the abilities of robot operators as they coordinate multiple robot agents during a mission. This was investigated in a pilot study using two inter-robot collaboration modalities and interface tools, which required different human-robot interaction techniques to execute a mission with a team of four robots. In the first modality, the operator manually inserted waypoints for each robot, as they acted as individual agents. In the second modality, the operator used the Planning Execution to After-Action Review (PETAAR) toolset to plot a single waypoint for the team of robots, as the robots coordinated their movement as a group. One novel component of this study is the investigation of how human-robot teaming dynamics and the PETAAR toolset impacted robot operators' real-time situation awareness and perceived cognitive load as well as team performance. Although the teaming modalities differed greatly with respect to the level of operator input needed, the time required to complete the simulation, the participant's perceived cognitive load, and interface usability were very similar for both modalities. In contrast, the results revealed statistically significant differences between the two teaming modalities related to participants' abilities to maintain a wedge formation while remaining situationally aware. Results from this work will be used to guide development of PETAAR along with the design of future studies investigating more complex teaming scenarios and for creating a baseline for comparing future results.

The HRI community is working to develop interactive robots for a wide variety of pro-social tasks and ideals. As such we naturally focus on the positive side of HRI including how robots and humans may collaborate and the benefits of doing so. This workshop, in contrast, will focus on the dark side of HRI with the goal of identifying, understanding and guarding against the potential negative consequences of interactive robots. The primary objective of the workshop is to articulate and discuss the most pertinent ethical issues facing the HRI community and to develop a set of common community guidelines.

In this paper, we introduce publicly available human-robot teaming datasets captured during the summer 2018 season using our Aquaticus testbed. Our Aquaticus testbed is designed to examine the interactions between human-human and human-robot teammates while situated in the marine environment in their own vehicles. In particular, we assess these interactions while humans and fully autonomous robots play a competitive game of capture the flag on the water. Our testbed is unique in that the humans are situated in the field with their fully autonomous robot teammates in vehicles that have similar dynamics. Having a competition on the water reduces the safety concerns and cost of performing similar experiments in the air or on the ground. By having the competitions on the water, we create a complex, dynamic, and partially observable view of the world for participants while in their motorized kayak. The main modality for teammate interaction is audio to better simulate the experience of real-world tactical situations - ie fighter pilots talking to each other over radios. We have released our complete datasets publicly so that we can enable researchers throughout the HRI community that do not have access to such a testbed and may have expertise other than our own to leverage our datasets to perform their own analysis and contribute to the HRI community.

Robotic rescuers digging through rubble, fire-fighting drones flying over populated areas, robotic servers pouring hot coffee for you, and a nursing robot checking your vitals are all examples of current or near-future situations where humans and robots are expected to interact in a dangerous situation. Dangerous HRI is an as-yet understudied area of the field. We define dangerous HRI as situations where humans experience some amount of risk of bodily harm while interacting with robots. This interaction could take many forms, such as a bystander (e.g. when an autonomous car waits at a crossing for a pedestrian), as a recipient of robotic assistance (rescue robots), or as a teammate (like an autonomous robot working with a SWAT team). To facilitate better study of this area, the Dangerous HRI workshop brings together researchers who perform experiments with some risk of bodily harm to participants and discuss strategies for mitigating this risk while still maintaining validity of the experiment. This workshop does not aim to tackle the general problem of human safety around robots, but instead focused on guidelines for and experience from experimenters.

Project Aquaticus is a human-robot teaming competition on the water involving autonomous surface vehicles and human operated motorized kayaks. Teams composed of both humans and robots share the same physical environment to play capture the flag. In this paper, we present results from seven competitions of our half-court (one participant versus one robot) game. We found that participants indicated more trust in more aggressive behaviors from robots.

Duckietown is an open, inexpensive and flexible platform for autonomy education and research. The platform comprises small autonomous vehicles (“Duckiebots”) built from off-the-shelf components, and cities (“Duckietowns”) complete with roads, signage, traffic lights, obstacles, and citizens (duckies) in need of transportation. The Duckietown platform offers a wide range of functionalities at a low cost. Duckiebots sense the world with only one monocular camera and perform all processing onboard with a Raspberry Pi 2, yet are able to: follow lanes while avoiding obstacles, pedestrians (duckies) and other Duckiebots, localize within a global map, navigate a city, and coordinate with other Duckiebots to avoid collisions. Duckietown is a useful tool since educators and researchers can save money and time by not having to develop all of the necessary supporting infrastructure and capabilities. All materials are available as open source, and the hope is that others in the community will adopt the platform for education and research.

Inferring the social structures of animal groups from their observed behavior is a non-trivial task usually handled by direct observation. Recent advances in sensing and tracking technology have enabled the collection of dense spatial data over long periods of time automatically. The qualitative differences between sparse hand-coded data and dense tracking data necessitate a new approach to inferring the social structure of the observed animals. We present a framework for using agent-based simulations to guide our approach to inferring social structure from tracking data collected from a small group of rhesus macaques over a period of three months. As part of this framework, we describe a version of the DOMWORLD model of dominance interactions in rhesus macaques that has been modified to include association preference, and adapted to more closely match the environment where the monkeys were housed. An exploration of simulation results reveals important characteristics of the tracking data. The inferred social structures of the tracked monkeys are also presented.

This paper presents a handheld mobile game, Grocery Hunter that encourages children to take on healthy eating habits. Children can use a pocket PC to play the Grocery Hunter game to learn about food nutrition and healthy food choices. Childhood obesity in the United States has already reached epidemic proportions. The best way to help children attain and maintain healthy weight is through physical activity and nutritious eating. Our design addresses nutrition directly by teaching children healthy eating habits using an interactive game in the grocery store.