This little six-legged robot has quite a repertoire of gait styles, and, like the Gambler, knows when to walk away and knows when to run thanks to its neural net processor.
Silke Steingrube of the Bernstein Centre for Computational Neuroscience in Göttingen, Germany, and colleagues disdained inflexible walking styles. Blessing their robotic creation with triple-jointed legs with sensors, and a neural net processor that determines the best gait, the determined little hexapod robot can make the choice that leads to the best progress depending on the terrain.
Using its raft of sensors – which detect foot-contact pressure, light, sound, heat and the robot's inclination – the robot can select the correct gait for uphill, downhill and various types of rough ground. By programming the robot to adopt the most energy-efficient gait possible, the researchers ensured it would switch gaits whenever its incline sensors were triggered. In tests, the robot taught itself 11 different walking styles. "The technique should work equally well in four-legged, six-legged or even wheeled robots," says Steingrube.
It has a flight reflex, too: if a rear sensor detects, say a very high temperature, it interprets this as a threat. "The neural network generates a fast, wave-like gait that is appropriate for running away," says Steingrube.
If the robot gets into difficulty, with a foot stuck in a hole, say, a number of sensors are stimulated. This creates a large input signal, which induces an unpredictable, chaotic output from the neural network, causing it to randomly choose one of its 11 gaits. In other words, the robot cycles through its repertoire until it frees itself.