  We describe a chemical robotic assistant equipped with a curiosity algorithm
(CA) that can efficiently explore the state a complex chemical system can
exhibit. The CA-robot is designed to explore formulations in an open-ended way
with no explicit optimization target. By applying the CA-robot to the study of
self-propelling multicomponent oil-in-water droplets, we are able to observe an
order of magnitude more variety of droplet behaviours than possible with a
random parameter search and given the same budget. We demonstrate that the
CA-robot enabled the discovery of a sudden and highly specific response of
droplets to slight temperature changes. Six modes of self-propelled droplets
motion were identified and classified using a time-temperature phase diagram
and probed using a variety of techniques including NMR. This work illustrates
how target free search can significantly increase the rate of unpredictable
observations leading to new discoveries with potential applications in
formulation chemistry.
