Photo: Melanie Gonick
Traditionally, programs that get robots to function autonomously have been split into two types: task planning and geometric motion planning. A task planner can decide that it needs to traverse the living room, but be unable to figure out a path around furniture and other obstacles. A geometric planner can figure out how to get to the phone, but not actually decide that a phone call needs to be made.
Of course, any robot that’s going to be useful around the house must have a way to integrate these two types of planning. Kaelbling and Lozano-Pérez believe that the key is to break the computationally burdensome larger goal into smaller steps, then make a detailed plan for only the first few, leaving the exact mechanisms of subsequent steps for later. “We’re introducing a hierarchy and being aggressive about breaking things up into manageable chunks,” Lozano-Pérez says. Though the idea of a hierarchy is not new, the researchers are applying an incremental breakdown to create a timeline for their “in the now” approach, in which robots follow the age-old wisdom of “one step at a time.”
The result is robots that are able to respond to environments that change over time due to external factors as well as their own actions. These robots “do the execution interleaved with the planning,” Kaelbling says.
The trick is figuring out exactly which decisions need to be made in advance, and which can — and should — be put off until later
Hierarchical Task and Motion Planning in the Now (8 pages)
Kaelbling compares this approach to the intuitive strategies humans use for complex activities. She cites flying from Boston to San Francisco as an example: You need an in-depth plan for arriving at Logan Airport on time, and perhaps you have some idea of how you will check in and board the plane. But you don’t bother to plan your path through the terminal once you arrive in San Francisco, because you probably don’t have advance knowledge of what the terminal looks like — and even if you did, the locations of obstacles such as people or baggage are bound to change in the meantime. Therefore, it would be better — necessary, even — to wait for more information.
Why shouldn’t robots use the same strategy?
Looking to the future, the researchers plan to build in learning algorithms so robots will be better able to judge which steps are OK to put off, and which ones should be dealt with earlier in the process. To demonstrate this, Kaelbling returns to the travel example: “If you’re going to rent a car in San Francisco, maybe that’s something you do need to plan in advance,” she says, because putting it off might present a problem down the road — for instance, if you arrive to find the agencies have run out of rental cars.
Although “household helper” robots are an obvious — and useful — application for this kind of algorithm, the researchers say their approach could work in a number of situations, including supply depots, military operations and surveillance activities.
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