When animals lose a limb, they learn to hobble remarkably quickly. And yet when robots damage a leg, they become completely incapacitated. That’s not so much of a problem for robots that are confined to factories where help is always on hand.

But robots have been evolving rapidly and are now on the verge of being able to cope with much more varied environments. In the next few years, we should see robots capable of domestic chores in the home, providing assistance to the elderly and of working side-by-side with humans in factories and offices.

It’s inevitable that these robots will become damaged. So how will they cope?

Today, Antoine Cully at the Sorbonne University in Paris and a couple of pals say they’ve developed a technique that allows a damaged robot to learn how to walk again in just a few seconds. They say their work has important consequences for the reliability and robustness of future robots and may also provide some insight into the way that animals adapt to injury as well.

First, some background. The problem of relearning how to walk when a leg becomes damaged is vastly complex. That’s because the number of parameters involved is huge. It involves the angle of each leg at the point where it bends, the position of each joint at every instant in time, the velocity and acceleration of these joints, the amount of time the leg spends in contact with the ground, the amount of force generated by each actuator involved in locomotion and so on.

So finding a new and effective walking pattern, or gait, requires a search through a very high dimensional space. That’s a length process because, in general, the only way to do it, is to try them one after the other.

So it’s possible to simulate the gait that results from one combination of parameters and assess its efficiency. The next step is to change one of the parameters and assess it again, change it further, then move on to another parameter and so on.

Cully and co take a different approach by attempting to reduce the size of the parameter space. So instead of calculating all this after the damage has occurred, these guys perform a good portion of the calculations in advance. They then use the results of this advanced calculation to reduce the dimensions of the parameter space.

That means that when the robot becomes damaged it can search through this low dimensional space for a new gait relatively quickly. And that dramatically speeds up the process of learning to walk again.

That’s the theory and Cully and co have put it to work on a six legged robot known as a hexapod. This robot has 18 motors to power its six legs. Consequently, its gait depends on 36 different parameters that describe things like the amplitude of the movement of each motor, the phase shifts between them, the duty cycle for each joint and so on.

The hexapod also has an on-board computer and a depth camera to estimate its walking speed. Its goal is to walk as fast as possible.

Before the robot is released into the wild, Cully and co calculate a behavioural repertoire of some 13,000 different gaits. However, they do not evaluate them; so the robot does not know how good each one is when a certain kind of damage occurs. Instead, the robot does this after it has become damaged.

Have performed this calculation, Cully and co then reduce this high dimensional data set to low dimensional one by considering only the amount of time that each leg spends in contact with the ground. That dramatically simplifies the problem of choosing new potential gaits when a leg becomes damaged.

So when the leg finally becomes damaged, the robot chooses a gait from the subset that minimises contact with the ground for that leg. It then measures how quickly it can walk using this gait.

It then uses this information to choose another gait, measures the resulting walking speed and feeds this information back into the model. After just a handful of tests, it can then select the best gait.

Cully and co first measure the walking performance of the robot with the standard alternating tripod gait in which the robot moves forward by first standing on three legs and then its other three legs and so on. This is the reference gait against which they can compare all the new ones.

The results are impressive. “When the robot is undamaged, our approach yields dynamic gaits that are 30% faster than the classic reference gait,” say Cully and co.

And they get better when the robot becomes damaged. The team say that the new compensatory gaits are up to seven times more efficient than the reference gait for that damaged condition. What’s more, the robot always finds a new gait in less than two minutes and often in just a matter of seconds.

Some of these gaits are entirely unexpected. Many involve dynamic movements in which the robot springs forward. In one case, the robot avoids using any of its feet by rolling onto its back and inching forward on its ‘shoulders’.

That’s impressive work that could have a significant impact on the reliability and robustness of walking robots. “This new technique will enable more robust, effective, autonomous robots,” they say.

Cully and co also think their technique provides some insight into the way that animals adapt to injuries. Of course, animals do not use the specific algorithm that the team has developed but there are parallels between the two approaches. Like animals, the robot does not have a predefined strategy how to cope with each possible injury but instead exploits intuitions about how its body works to find a new gait. “Also like animals, intelligent trial and error allows the quick identification of working behaviours with a few, diverse tests,” they say.

That’s an interesting approach that could have a significant impact on the way robots perform in the real world. One thing is for sure, better robots are coming—it’s only a matter of time before they play a much larger role in each one of our lives.

Ref: arxiv.org/abs/1407.3501 : Robots That Can Adapt Like Natural Animals