A Walking Robot Goes Mountaineering

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A Walking Robot Goes Mountaineering

http://www.medicalnewstoday.com/medicalnews.php?newsid=76732

The human gait is a marvel of coordination. All aspects of movement

control - from the angle of the knee joints to the momentum of the

hip up to the balance point of the torso - need to be meticulously

adjusted. In addition, the gait is adaptable to different

environments. Walking on ice is different from walking on solid

ground, walking uphill is different from downhill.

In their study, publishing in PLoS Computational Biology July 13,

2007, scientists around Florentin Wörgötter, Bernstein Center for

Computational Neuroscience at the University of Göttingen, have

simulated the neuronal principles that form the basis of this

adaptivity in a walking robot. " RunBot " , as it is called, lives up

to its name - it holds the world record in speed walking for dynamic

machines. Now its inventors have expanded its repertoire. With an

infrared eye it can detect a slope on its path and adjust its gait

on the spot. Just as a human, it leans forwards slightly and uses

shorter steps. It can learn this behavior using only a few trials.

The robots ability to abruptly switch from one gait to the other is

due to the hierarchical organization of the movement control. In

this respect, it resembles that of a human and can hold as a human

model. On the lower hierarchical levels, movement is based on

reflexes driven by peripheral sensors. Control circuits ensure that

the joints are not overstretched or that the next step is initiated

as soon as the foot touches the ground. Only when the gait needs to

be adapted, higher centers of organization step in - a process

triggered by the human brain or, in case of the robot, by its

infrared eye leading on to a simpler neural network. Because of the

hierarchical organization adjustment of the gait can be achieved by

changing only a few parameters. Other factors will be automatically

tuned through the regular circuits.

At its first attempt to climb a slope, RunBot will fall over

backwards, as it has not yet learned to react to its visual input

with a change in gait. But just like children, RunBot learns from

its failures, leading to a strengthening of the contact between the

eye and the sites of movement control. Only once these connections

are established, step length and body posture are controllable by

the visually induced signal. The steeper the slope, the stronger

RunBot will adapt its gait.

" Adaptive, fast walking in a biped robot under neuronal control and

learning. "

Manoonpong P, Geng T, Kulvicius T, Porr B, Wo¨ rgo¨ tter F (2007)

PLoS Comput Biol 3(7): e134. doi:10.1371/journal.pcbi.0030134