A specific aspect of ML is that robot responses have to happen in real time, without delays, so that the actions taken are effective. For example, if it finds an obstacle crossing the path it is following, we expect that it avoids it. To do so, obstacle identification has to occur as it appears in the robot's field of view. Hence, the subsequent action of avoiding the obstacle has to be taken immediately to avoid a crash.

We will support our methodology description with an end-to-end example that covers all that GoPiGo3 can do up to this point. Then, with this example, we expect that GoPiGo3 can carry a load on top of its chassis from its current location to a target location (a common case in garbage collector robots).