Extended Data Figure 1: An overview of the IT&E algorithm.

a, Behaviour–performance map creation. After being initialized with random controllers, the behavioural map (a2), which stores the highest-performing controller found so far of each behaviour type, is improved by repeating the process depicted in a1 until newly generated controllers are rarely good enough to be added to the map (here, after 40 million evaluations). This step, which occurs in simulation (and required roughly two weeks on one multi-core computer; see Supplementary Methods, section ‘Running time’), is computationally expensive, but only needs to be performed once per robot (or robot design) before the robot is deployed. b, Adaptation. In b1, each behaviour from the behaviour–performance map has an expected performance based on its performance in simulation (dark green line) and an estimate of uncertainty regarding this predicted performance (light green band). The actual performance on the now-damaged robot (black dashed line) is unknown to the algorithm. A behaviour is selected for the damaged robot to try. This selection is made by balancing exploitation—trying behaviours expected to perform well—and exploration—trying behaviours whose performance is uncertain (Supplementary Methods, section ‘Acquisition function’). Because all points initially have equal, maximal uncertainty, the first point chosen is that with the highest expected performance. Once this behaviour is tested on the physical robot (b4), the performance predicted for that behaviour is set to its actual performance, the uncertainty regarding that prediction is lowered, and the predictions for, and uncertainties about, nearby controllers are also updated (according to a Gaussian process model, see Supplementary Methods, section ‘Kernel function’), the results of which can be seen in b2. The process is then repeated until performance on the damaged robot is 90% or greater of the maximum expected performance for any behaviour (b3). This performance threshold (orange dashed line) lowers as the maximum expected performance (the highest point on the dark green line) is lowered, which occurs when physical tests on the robot underperform expectations, as occurred in b2.