The paper introduces an intelligent trial and error (IT&E) algorithm that enables robots to adapt to damage in less than two minutes without requiring self-diagnosis or pre-specified contingency plans. The key insight is that robots can exploit a detailed behavior-performance map, which represents their intuitions about what behaviors they can perform and their value. If the robot is damaged, it uses this map to guide a trial-and-error learning algorithm that conducts intelligent experiments to rapidly discover compensatory behaviors. Experiments on a hexapod robot and a robotic arm demonstrate successful adaptations for various types of damage, including broken or missing legs and joint failures. This approach enables more robust and effective autonomous robots and suggests principles that animals may use to adapt to injury.The paper introduces an intelligent trial and error (IT&E) algorithm that enables robots to adapt to damage in less than two minutes without requiring self-diagnosis or pre-specified contingency plans. The key insight is that robots can exploit a detailed behavior-performance map, which represents their intuitions about what behaviors they can perform and their value. If the robot is damaged, it uses this map to guide a trial-and-error learning algorithm that conducts intelligent experiments to rapidly discover compensatory behaviors. Experiments on a hexapod robot and a robotic arm demonstrate successful adaptations for various types of damage, including broken or missing legs and joint failures. This approach enables more robust and effective autonomous robots and suggests principles that animals may use to adapt to injury.