The atomistic Green's function method (AGF) has emerged as a useful tool to study phonon transport across interfaces. A comprehensive review of developments in the AGF method over the last decade is provided in this chapter. The content includes a discussion of the fundamentals of a Green's function starting from a continuum viewpoint and extending it to the atomistic regime. Comprehensive derivations of the AGF equations (within the harmonic framework) are presented along with intuitive physical explanations for the various matrices involved. The numerical issues in computational implementation of the various mathematical equations are illustrated with a one-dimensional atom chain example. The application of the AGF method to dimensionally mismatched and bulk interfaces and the process of obtaining polarization-specific transmission functions are illustrated with examples. Recent advancements such as integration of the AGF method with other tools (such as density functional theory and Boltzmann transport equation solvers) and extension of the AGF method to include anharmonicity are also presented. Comparisons of results from the AGF method to experimental measurements for superlattice and metal-graphene interfaces are provided.

In this paper, we present a planning framework that uses a combination of implicit (robot motion) and explicit (visual/audio/haptic feedback) communication during mobile robot navigation in a manner that humans find understandable and trustworthy. First, we developed a model that approximates both continuous movements and discrete decisions in human navigation, considering the effects of implicit and explicit communication on human decision making. The model approximates the human as an optimal agent, with a reward function obtained through inverse reinforcement learning. Second, a planner uses this model to generate communicative actions that maximize the robot's transparency and efficiency. We implemented the planner on a mobile robot, using a wearable haptic device for explicit communication. In a user study of navigation in an indoor environment, the robot was able to actively communicate its intent to users in order to avoid collisions and facilitate efficient trajectories. Results showed that the planner generated plans that were easier to understand, reduced users' effort, and increased users trust of the robot, compared to simply performing collision avoidance.

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Automation of repetitive tasks can improve laparoscopic surgical procedures by unloading surgeons and reducing duration, trauma, and expense. However, surgical procedures involve delicate manipulation of deformable tissues in a very dynamic environment, suggesting that automated execution of surgical tasks should be carried out under the supervision of the surgeon. We propose a teleoperated architecture that allows a surgeon to employ and supervise agents that can autonomously perform or assist with surgical tasks. The architecture is independent of the automation method. It includes a dominance factor that allows the surgeon to take control over the slave robot at any time, and an aggressiveness factor that sets the performance pace of the autonomous agent. We tested the architecture during execution of a multilateral tension-and-cut task, where a human operator and an autonomous agent are responsible for tensioning or cutting of a tissue. The architecture allowed for supervised and paced automation of the task. We found that collaboration of the human operator and autonomous agent can lead to shorter completion time compared to performance of only a human.