Taha Shaheen

Explicit communication is often valued for its directness in presenting information but requires attention during exchange, resulting in cognitive interruptions. On the other hand, implicit communication contributes to tacit and smooth interaction, making it more suitable for teaming, but requires inference for interpretation. This paper studies a novel type of implicit visual communication (IVC) using shadows via visual projection with augmented reality, referred to as active shadowing (ASD). Prior IVC methods, such as legible motion, are often used to influence the perception of robot behavior to make it more understandable. They often require changing the physical robot behavior, resulting in suboptimality. In our work, we investigate how ASD can be used to achieve similar effects without losing optimality. Our evaluations with user studies demonstrates that ASD can effectively creates ”illusions” that maintain optimal physical behavior without compromising its understandability. We also show that ASD can be more informative than other explicit communication methods, and examine the conditions under which ASD becomes less effective.

One simplifying assumption in existing and well-performing task allocation methods is that the robots are single-tasking: each robot operates on a single task at any given time. While this assumption is harmless to make in some situations, it can be inefficient or even infeasible in others. In this paper, we consider assigning multi-robot tasks to multitasking robots. The key contribution is a novel task allocation framework that incorporates the consideration of physical constraints introduced by multitasking. This is in contrast to the existing work where such constraints are largely ignored. After formulating the problem, we propose a compilation to weighted MAX-SAT, which allows us to leverage existing solvers for a solution. A more efficient greedy heuristic is then introduced. For evaluation, we first compare our methods with a modern baseline that is efficient for single-tasking robots to validate the benefits of multitasking in synthetic domains. Then, using a site-clearing scenario in simulation, we further illustrate the complex task interaction considered by the multitasking robots in our approach to demonstrate its performance. Finally, we demonstrate a physical experiment to show how multitasking enabled by our approach can benefit task efficiency in a realistic setting.

Avatar robots allow a teleoperator to interact with the people and environment of a remote place. Malicious operators can use this technology to perpetrate malicious or low-moral actions. In this study, we used hazard identification workshops to identify low-moral actions that are possible through the locomotor movement, cameras, and microphones of an avatar robot. We conducted three workshops, each with four potential future users of avatars, to brainstorm possible low-moral actions. As avatars are not yet widespread, we gave participants experience with this technology by having them control both a simulated avatar and a real avatar as a malicious anonymous operator in a variety of situations. They also experienced sharing space with an avatar controlled by a malicious anonymous operator. We categorized the ideas generated from the workshops using affinity diagram analysis and identified four major categories: violate privacy and security, inhibit, annoy, and destroy or hurt. We also identified subcategories for each. In the second half of this study, we discuss all low-moral action subcategories in terms of their detection, mitigation, and prevention by studying literature from autonomous, social, teleoperated, and telepresence robots as well as other fields where relevant.