It used to be that even sophisticated mobile robots could be easily defeated by using (say) a table to block its way. The robot would sense the table, categorize it as an obstacle, try to plan a path around it, and then give up when its planner fails. This works because robots generally don’t know what most objects are, or how they work, or what you can do with them: They just get turned into obstacles to be avoided, because in most cases, that’s the easiest and safest thing to do.
You can’t normally use a table across a hallway to deter a human, because humans understand that tables are physical objects that can be moved, and the human will just pull the table out of the way and keep on going. Even if the table doesn’t behave exactly the way we’d expect it to (like, one of the wheels is stuck), we can adapt, and figure it out.
At IROS 2016 in South Korea, Jonathan Scholz from Google DeepMind and collaborators from Georgia Tech presented a paper on “Navigation Among Movable Obstacles with Learned Dynamic Constraints,” which gives mobile manipulators this same capability. They can recognize objects in their way, and get inventive with physics-based tricks to get where they need to go.