Robots that can adapt like animals

An intelligent trial-and-error learning algorithm is presented that allows robots to adapt in minutes to compensate for a wide variety of types of damage. Robots built to adapt Autonomous mobile robots would be extremely useful in remote or hostile environments such as space, deep oceans or disaster areas. An outstanding challenge is to make such robots able to recover after damage. Jean-Baptiste Mouret and colleagues have developed a machine learning algorithm that enables damaged robots to quickly regain their ability to perform tasks. When they sustain damage — such as broken or even missing legs — the robots adopt an intelligent trial-and-error approach, trying out possible behaviours that they calculate to be potentially high-performing. After a handful of such experiments they discover, in less than two minutes, a compensatory behaviour that works in spite of the damage. Robots have transformed many industries, most notably manufacturing 1 , and have the power to deliver tremendous benefits to society, such as in search and rescue 2 , disaster response 3 , health care 4 and transportation 5 . They are also invaluable tools for scientific exploration in environments inaccessible to humans, from distant planets 6 to deep oceans 7 . A major obstacle to their widespread adoption in more complex environments outside factories is their fragility 6 , 8 . 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 9 , and require a pre-programmed contingency plan for every type of potential damage, an impracticality for complex robots 6 , 8 . A promising approach to reducing robot fragility involves having robots learn appropriate behaviours in response to damage 10 , 11 , but current techniques are slow even with small, constrained search spaces 12 . 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