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 fragility6,8. 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 modes9, and require a pre-programmed contingency plan for every type of potential damage, an impracticality for complex robots6,8. A promising approach to reducing robot fragility involves having robots learn appropriate behaviours in response to damage10,11, 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.
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We thank L. Tedesco, S. Doncieux, N. Bredeche, S. Whiteson, R. Calandra, J. Droulez, P. Bessière, F. Lesaint, C. Thurat, S. Ivaldi, C. Lan Sun Luk, J. Li, J. Huizinga, R. Velez, H. Mengistu, M. Norouzzadeh, T. Clune, and A. Nguyen for feedback and discussions. This work has been funded by the ANR Creadapt project (ANR-12-JS03-0009), the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement number 637972), and a Direction Générale de l’Armement (DGA) scholarship to A.C.
Extended data figures
This video shows the Intelligent Trial and Error Algorithm in action on the two experimental robots in this paper: a hexapod robot and a robotic arm (Fig. 3). The video shows several examples of the different types of behaviours that are produced during the behaviour-performance map creation step, from classic hexapod gaits to more unexpected forms of locomotion. Then, it shows how the hexapod robot uses that behaviour-performance map to adapt to damage that deprives one of its leg of power (Fig. 3a:C3). The video also illustrates how the Intelligent Trial and Error Algorithm also finds a compensatory behaviour for the robot arm. Finally, adaptation to a second damage condition is shown for both the hexapod and robotic arm.
In the behavior-performance map creation step, the MAP-Elites algorithm produces a diverse collection of different types of walking gaits. The video shows several examples of the different types of behaviors that are produced, from classic hexapod gaits to more unexpected forms of locomotion.