Walking robot goes mountaineering

 

Scientists have succeeded in simulating the complex aspects of human movement in a walking robot.

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The team, led by Florentin Wörgötter at the Bernstein Center for Computational Neuroscience at the University of Göttingen, has simulated the neuronal principles that form the basis of human gait adaptability in a 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 behaviour using only a few trials.

The robot's ability to abruptly switch from one gait to the other is due to the hierarchical organisation 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.

Just like children, RunBot learns from its failures.

Only when the gait needs to be adapted, higher centres of organisation 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 organisation adjustment, the gait can be achieved by changing only a few parameters. Other factors will be automatically tuned through the regular circuits.

Picking itself up

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 after 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.

The study was published in the 13 July edition of the journal PLoS Computational Biology (http://www.ploscompbiol.org/).

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