Technique could enable robots to navigate pushy crowds, congested streets

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.