Alphabet's X team for moonshot projects has been using robots to sort compost, recycling, and landfill waste in initial use at X company offices in recent months. The robots on wheels can drive to trash sites in the Mountain View, California office and sort recycling and trash using a combination of computer vision and a robotic arms. The news today came as Alphabet unveiled the Everyday Robot Project, a moonshot to make robots augment human activity in the physical world in environments like the home or office the same way computers do in the virtual world. The Everyday Robot Project has been underway for years, as many members of the team that collaborates with Google AI joined Alphabet in 2015 or 2016. "During the last few months, our robots have sorted thousands of pieces of trash and reduced our office's waste contamination levels from 20% -- which is what it is when people put objects in the trays -- to less than 5%," Alphabet X robotics project lead Hans Peter Brondmo said in a post today.

Today, Alphabet's X moonshot division (formerly known as Google X) unveiled the Everyday Robot project, whose goal is to develop a "general-purpose learning robot." The idea is that its robots could use cameras and complex machine learning algorithms to see and learn from the world around them without needing to be coded for every individual movement. The team is testing robots that can help out in workplace environments, though right now, these early robots are focused on learning how to sort trash. Here's what one of them looks like -- it reminds me of a very tall, one-armed Wall-E (ironic, given what the robots are tasked to do): The concept of grasping something comes pretty easily to most humans, but it's a very challenging thing to teach a robot, and Everyday Robot's robots get their practice in both the physical world and the virtual world. In a tour of X's offices, Wired described how a "playpen" of nearly 30 of the robots (supervised by humans) spend their daytime hours sorting trash into trays for compost, landfill, and recycling.

The world of robotics today is a lot like the world of computing 50 years ago. There's lots of talk and optimism about what robots could do to help people in their everyday lives, yet that future is still a ways off. Most robots are where mainframe computers were in the '60s and '70s: expensive specialist machines, operated by experts, performing specialized tasks in specially-designed environments. A series of innovations, most notably the invention of the microprocessor, saw computers become cheaper, smaller, and much more powerful than anyone had thought possible. I believe we're on the cusp of a similar transition in robotics.

Google's Alphabet is adding yet another chapter to its rollercoaster history of robotics projects. The company's R&D lab, dubbed Alphabet X, is working on a new breed of robots that can learn new tasks, rather than be programmed into performing them. The initiative is called the "Everyday Robot Project" because, as its name implies, it wants to build robots that can assist humans in simple tasks of everyday life. More importantly, the machines could do so even when confronted with our messy, unpredictable environments. The leader of the project, Hans Peter Brondmo, explained that robots currently operate in environments specifically designed and structured for them.

This week, Google announced that it has trained robots to sort through office trash and remove items that should go in recycling or the compost bin. The breakthrough came from Google X's Everyday Robot Project, an open-ended research initiative aimed at trying to integrate robots into daily life. Over the last few months, Google's office robots have decreased waste contamination levels from 20 percent to just five percent. Waste contamination levels are a measure of how much improperly sorted material there is mixed into the trash. For the trash sorting project, Google's decided that instead of trying to write complicated instructions for how to identify different kinds of items, they would see if the robots could just figure things out through trial and error.