Smooth, it isn't. The star-shaped robot lurches, wheezes and flops through its ponderous perambulation, clacking laboriously but steadily across the table. But for this machine, developed by Joshua Bongard, assistant professor of computer science, the breakthrough is the journey, not the destination.
The machine, which Bongard worked on at Cornell University with then-colleagues Victor Zykov and Hod Lipson, is the first robot capable of detecting its own shape and using this knowledge to efficiently adapt to damage. The work was reported by the group in a November 2006 issue of Science.
Around the same time, Bongard also published a co-authored MIT Press book, "How the Body Shapes the Way We Think: A New View of Intelligence" with lead author Rolf Pfeifer, and, in ways perhaps evocative of the text's title, the robot's breakthrough is its physical self-awareness and adaptability.
The machine, Bongard explains, starts out having no sense of how its parts are assembled. It measures the results of a limited number of movements to develop plausible models of its construction. The robot refines these competing models through more movements and observation, eventually arriving at an accurate internal model of its shape. The robot can then use this continuously updated self-model to detect damage and develop new ways to move.
"This robot starts to suggest something about the nature of curiosity, in the sense that the robot, when it's learning about itself, doesn't simply thrash around randomly," says Bongard. "It actually tries out each time a new action to try to learn something new about its own body and its local environment. In a sense, at a very rudimentary level, this robot is curious."
The work of Bongard and colleagues is a proof-of-concept for developing more resilient robots for dangerous applications like planetary exploration. Bongard continues his research in the field at UVM, where he is introducing robotics work into upper-level computer science courses.