Video: For the first time, a modular robot has been shown to adapt when some components fail

Roombots in one of a multitude of possible configurations

Think that shape-shifting robots, or ones that march on no matter how many limbs they lose, are just for Terminator films? Think again. A team of European roboticists have developed software that allows a modular robot to adapt when one part stops working.

David Johan Christensen at the University of Southern Denmark in Odense, working with Alexander Spröwitz and Auke Ijspeert at the Swiss Federal Institute of Technology in Lausanne, simulated a quadruped robot constructed from a dozen Roombots – identical rounded robots that have been developed in Lausanne and which can combine to form a variety of modular shapes (see picture).

In the simulation, each Roombot alters its pattern of movement randomly every few seconds and assesses how those changes affect the quadruped’s overall velocity. After being given 10 minutes to find its feet, the quadruped had increased its speed from 5 centimetres per second to 31 cm/s.

When one Roombot was then made to malfunction – instantly slashing the walking speed to 15 cm/s – the quadruped learned to adapt its gait. After a further 20 minutes the hobbled robot had increased its walking speed to 21 cm/s.


Damage control

The virtual quadruped is not the first robot to learn to adapt after damage. In 2006, Josh Bongard of the University of Vermont in Burlington worked with Viktor Zykov and Hod Lipson of Cornell University in Ithaca, New York, to design a multi-legged robot that uses knowledge of itself to work out how to adapt its gait if one leg malfunctions.

“The main difference is that our robot has no internal model of itself or the environment, and there is no centralised ‘brain’, but only a number of independently learning modules,” says Christensen. “We have demonstrated that it is possible to achieve the same level of adaptation” without needing such a brain, he says.

Lipson thinks that Christensen’s team’s new work is both interesting and promising. “This method is especially suited for distributed systems such as modular robots,” he says.

Bongard agrees, and adds that the two adaptive approaches could prove complementary. “A robot could adapt its pattern of locomotion while moving, yet at the same time use its self-model to mentally simulate new kinds of behaviours,” he says. “This would be similar to the way a hiker might continuously adapt her way of walking on a rocky path while thinking about how to climb up an approaching steep ascent.”

Christensen presented the work at the Simulation of Adaptive Behaviour conference in Paris, France, last week.

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