Recent advances in computer architecture and networking have ushered in a new age of edge computing, where computation is placed close to the point of data collection to facilitate low-latency decision making. As the complexity of such deployments grow into networks of interconnected edge devices, getting the necessary data to be in "the right place at the right time" can become a challenge. We envision a future of edge analytics where data flows between edge nodes are declaratively configured through high-level constraints. Using machine learning model-serving as a prototypical task, we illustrate how the heterogeneity and specialization of edge devices can lead to complex, task-specific communication patterns even in relatively simple situations. Without a declarative framework, managing this complexity will be challenging for developers and will lead to brittle systems. We conclude with a research vision for database community that brings our perspective to the emergent area of edge computing.

In this paper, we address the problem of estimating the shape of a planar elastic rod (e.g., a thin flexible strip of metal) using images of the rod. This is done by treating configurations of the elastic rod as solutions of a geometric optimal control problem. The necessary conditions for optimality provide coordinates over which to perform inference, and the sufficient conditions provide the gradient of the shape of the rod with respect to these coordinates. This optimal control formulation allows for configurations of the rod to be represented as points in a finite-dimensional space without having to discretize the shape of the rod. We consider the estimation problem with and without fiducial markers attached to the rod. Results from both simulations and hardware experiments demonstrate the ability of our approach to track the shape of a deforming elastic rod.

In this paper, we introduce and experimentally validate a sampling-based planning algorithm for quasi-static manipulation of a planar elastic rod. Our algorithm is an immediate consequence of deriving a global coordinate chart of finite dimension that suffices to describe all possible configurations of the rod that can be placed in static equilibrium by fixing the position and orientation of each end. Hardware experiments confirm this derivation in the case where the “rod” is a thin, flexible strip of metal that has a fixed base and that is held at the other end by an industrial robot. We show an example in which a path of the robot that was planned by our algorithm causes the metal strip to move between given start and goal configurations while remaining in quasi-static equilibrium.