MIT engineers working on contextual navigation to enable robots deliver to your doorstep

Image Source: MIT News

Traditional navigation systems used by delivery robots use the approach where areas are pre-mapped and then algorithms guide the robot to reach locations in that mapped area. However, as can be understood, it is not possible to map the path to every front door of every house. Doing that would also raise a lot of privacy issues.

We humans do not need to memorize the layout and orientation of every house in a locality when we want to reach the front door of any given house in that locality. We can figure it out when we are able to locate things like the garage, driveway, lawn gate etc. MIT engineers working at the Aerospace Controls Lab believe this approach is exactly what is needed to solve automated last-mile delivery problems.

The power of machine learning allows the MIT engineers to train the AI in the robots to recognise  clues in its environment such as driveway, sidewalk, etc. to plan out a route to its intended destination. For example, if a robot is instructed to deliver a package to someone's front door, it might start on the road and see a driveway, which it has been trained to recognize as likely to lead toward a sidewalk, which in turn is likely to lead to the front door.

“We wouldn’t want to have to make a map of every building that we’d need to visit,” says Michael Everett, a graduate student in MIT’s Department of Mechanical Engineering. “With this technique, we hope to drop a robot at the end of any driveway and have it find a door.”

Here is video demonstration of the technology at its current state:

At the core of the technology is the process to teach robots to recognize objects by their semantic label. An example offered is the robot recognizing a door as a door and not as a solid rectangular obstacle. The researchers used an algorithm to build up a map of the environment as the robot moved around, using the semantic labels of each object and a depth image. This algorithm is called semantic SLAM (Simultaneous Localization and Mapping). 

This algorithm goes a step further than similar existing algorithms in the facts that it not only maps the objects that it recognizes in its local environment but also tries to figure out the most efficient path to it's semantically learned destination.

Imagine if delivery robots start to interact with elevators over standardized communication protocols and they learn to interpret signage, then they may even be able to deliver to doors of apartments in high-rises. Maybe one day, advancement of this approach will even lead to drones recognising balconies of apartments. Imagine living on the 15th floor and getting your pizza drone delivered right to your balcony!