5 paths to the walking, talking, pie-baking humanoid robot

Path 3: Navigation

As Jeeves hustles through the kitchen with the beer, the family dog, a yapping terrier who hasn't yet adjusted to the new help, plants itself in a doorway and refuses to budge. The robot, avoiding confrontation, opts for an alternative route. He turns, steps over a childproof gate into the family room, and encounters a minefield: blocks and stuffed animals strewn about by Jack's two-year-old. Jeeves has a standing order to pick up such things-in addition to vacuuming, scrubbing the floors, dusting, and washing the dishes-but the beer is a higher priority right now. He gingerly steps through the room, careful to avoid each toy, and delivers the bottle.

There's significant debate among roboticists about whether a robo-butler would need legs, since even wheelchairs can now climb stairs, but there are some distinct advantages to the bipedal approach. A wheeled humanoid might have to clear a path for itself before rolling through a cluttered room. A biped could just tiptoe its way through, and also climb over obstacles.

Whether walking or rolling, the robo-butler is going to have to find its way around. First, it will need the right hardware to sense obstacles. Some
scientists advocate loading humanoids with sensors-laser range finders, infrared, 3-D vision-that would provide a detailed, continuously updated, 360-degree model of the shape, size and placement of everything in a room. Then there are the purists who believe that a humanoid should not have abilities above and beyond the most direct human equivalent. This view, more common in Japan than in the U.S., is a quasi-philosophical devotion to the challenge of building a mechanized human. This camp says that a robo-butler should walk on two legs because that's how humans do it, not because legs are better than wheels. To this group, dispatching laser pulses to estimate the distance of objects instead of just relying on binocular vision, as humans do, would undermine the effort to replicate human abilities in a machine. It would be cheating.

James Kuffner, who does path-planning work with both virtual and real humanoids, likens the decision-making process associated with robot navigation to chess. When crossing a room, the robot uses object-recognition software to help it determine what could be moved out of the way and what it would need to circumvent-an ottoman versus a large couch. Each potential step is considered in terms of the long-term outcome: getting to the other side of the room. The robot picks a path that is the right mix of fast, safe and efficient, and starts to walk. All the while, it updates its model of the environment, checking to see if anything has changed and ensuring that it has chosen the best route.