FedEx Robot Solves Complex Packing Problems

Despite the fact that it constantly seems like we’re in the midst of a robotics- and artificial intelligence-driven revolution, there are a number of tasks that continue to elude even the best machine learning algorithms and robots. The clothing industry is an excellent example, where the flimsy materials can easily trip up robotic manipulators. But one task like this that seems like it might soon be solve is packing cargo into trucks, as FedEx is trying to do with one of their new robots.

Part of the reason this task is so difficult is that packing problems, similar to “traveling salesman” problems, are surprisingly complex. The packages are not presented to the robot in any particular order, and need to be efficiently placed according to weight and size. This robot, called DexR, uses artificial intelligence paired with an array of sensors to get an idea of each package’s dimensions, which allows it to then plan stacking and ordering configurations and ensure secure fits between all of the other packages. The robot must also be capable of quickly adapting if any packages shift during stacking and re-order or re-stack them.

As robotics platforms and artificial intelligence continue to improve, it’s likely we’ll see a flurry of complex problems like these solved by machine instead of by human. Tackling real-world tasks are often more complex than they seem, as anyone with a printer an a PC LOAD LETTER error can attest to, even handling single sheets of paper can be a difficult task for a robot. Interfacing with these types of robots can be a walk in the park, though, provided you read the documentation first.

A Cheat Sheet For Publishing Python Packages

[Brendan Herger] was warned that the process of publishing a Python package would be challenging. He relishes a challenge, however, and so he went at it with gusto. The exhausting process led him to share a cheat sheet for publishing Python packages with the goal of making the next time smoother, while also letting other people benefit from his experience and get a running start.

[Brendan] describes publishing a Python package as “tying together many different solutions with brittle interchanges.” His cheat sheet takes the form of an ordered workflow for getting everything in place, with some important decisions and suggestions about things like formatting and continuous integration (CI) made up-front.

The guide is brief, but [Brendan] has made errors and hit dead ends in the hopes that others won’t have to. The whole thing came about from his work in deep learning, and his desire to create a package that allows rapid building and iterating on deep learning models.

Deep learning is a type of machine learning that involves finding representations in large amounts of data. [Brendan] used it in a project to automatically decide whether a Reddit post contains Star Wars plot spoilers, and we recently saw it featured in a method of capturing video footage only if a hummingbird is present.