Emulating animals, these robots can recover from damage in two minutes

The kind of robot you'd want to take on a hazardous mission
June 1, 2015

Researchers in France and the U.S. have developed a new technology that enables robots to quickly recover from an injury in less than two minutes, similar to how injured animals adapt. Such autonomous mobile robots would be useful in remote or hostile environments such as disaster areas, space, and deep oceans.

The video above shows a six-legged robot that adapts to keep walking even if two of its legs are broken. It also shows a robotic arm that learned how to correctly place an object even with several broken motors.

“When injured, animals do not start learning from scratch,” says Jean-Baptiste Mouret from Pierre and Marie Curie University. “Instead, they have intuitions about different ways to behave. These intuitions allow them to intelligently select a few, different behaviors to try out and, after these tests, they choose one that works in spite of the injury. We made robots that can do the same.”

The researchers developed an “Intelligent Trial and Error” algorithm that allows robots to emulate animals: the robots conduct experiments to rapidly discover a compensatory behavior that works despite the damage.

“For example, if walking, mostly on its hind legs, does not work well, it will next try walking mostly on its front legs,”  explains Antoine Cully, lead author of a May 28 cover article on this research in the journal Nature. “What’s surprising is how quickly it can learn a new way to walk. It’s amazing to watch a robot go from crippled and flailing around to efficiently limping away in about two minutes.”

Abstract of Robots that can adapt like animals.

Robots have transformed many industries, most notably manufacturing1, and have the power to deliver tremendous benefits to society, such as in search and rescue2, disaster response3, health care4 and transportation5. They are also invaluable tools for scientific exploration in environments inaccessible to humans, from distant planets6 to deep oceans7. A major obstacle to their widespread adoption in more complex environments outside factories is their fragility68. Whereas animals can quickly adapt to injuries, current robots cannot ‘think outside the box’ to find a compensatory behaviour when they are damaged: they are limited to their pre-specified self-sensing abilities, can diagnose only anticipated failure modes9, and require a pre-programmed contingency plan for every type of potential damage, an impracticality for complex robots68. A promising approach to reducing robot fragility involves having robots learn appropriate behaviours in response to damage1011, but current techniques are slow even with small, constrained search spaces12. Here we introduce an intelligent trial-and-error algorithm that allows robots to adapt to damage in less than two minutes in large search spaces without requiring self-diagnosis or pre-specified contingency plans. Before the robot is deployed, it uses a novel technique to create a detailed map of the space of high-performing behaviours. This map represents the robot’s prior knowledge about what behaviours it can perform and their value. When the robot is damaged, it uses this prior knowledge to guide a trial-and-error learning algorithm that conducts intelligent experiments to rapidly discover a behaviour that compensates for the damage. Experiments reveal successful adaptations for a legged robot injured in five different ways, including damaged, broken, and missing legs, and for a robotic arm with joints broken in 14 different ways. This new algorithm will enable more robust, effective, autonomous robots, and may shed light on the principles that animals use to adapt to injury.