Robots have transformed many industries, most notably manufacturing, and have the
power to deliver tremendous benefits to society, such as in search and rescue, disaster
response, health care and transportation. They are also invaluable tools for scientific
exploration in environments inaccessible to humans, from distant planets to deep oceans.
A major obstacle to their widespread adoption in more complex environments outside
factories is their fragility. 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 modes, and require a pre-programmed contingency plan for
every type of potential damage, an impracticality for complex robots. A promising
approach to reducing robot fragility involves having robots learn appropriate behaviours
in response to damage, but current techniques are slow even with small, constrained
search spaces. 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.