The basal ganglia system has been proposed as a possible neural substrate for action selection in the vertebrate brain. We describe a robotic implementation of a model of the basal ganglia and demonstrate the capacity of this system to generate adaptive switching between several acts when embedded in a robot that has to ‘survive' in a laboratory environment. A comparison between this brain-inspired selection mechanism and classical `winner-takes-all\' selection highlights some adaptive properties specific to the model, such as avoidance of dithering and energy-saving. These properties derive, in part, from the capacity of simulated basal ganglia-thalamo-cortical loops to generate appropriate ‘behavioural persistence'.