Robots are great at dealing with predictable environments, but human pedestrian behavior can be difficult to anticipate. That's especially true in the frenzy to catch the D train at rush hour. A group of MIT researchers is on the case and adding to a growing body of academic work aiming to give robots some of the tools we (at least those of us living in overcrowded cities) take for granted: Street intuition. In a paper entitled "Deep sequential models for sampling-based planning," the researchers outline a method of robot navigation that utilizes traditional path planning algorithms, which analyze a number of options in real time and select the optimal choice, with a neural network that learns over time by observing and interacting with people. The addition of a neural network remedies a problem with traditional path planning, which relies on a branching decision tree that evaluates environmental conditions.