  Autonomous robotic arm manipulators have the potential to make planetary
exploration and in-situ resource utilization missions more time efficient and
productive, as the manipulator can handle the objects itself and perform
goal-specific actions. We train a manipulator to autonomously study objects of
which it has no prior knowledge, such as planetary rocks. This is achieved
using causal machine learning in a simulated planetary environment. Here, the
manipulator interacts with objects, and classifies them based on differing
causal factors. These are parameters, such as mass or friction coefficient,
that causally determine the outcomes of its interactions. Through reinforcement
learning, the manipulator learns to interact in ways that reveal the underlying
causal factors. We show that this method works even without any prior knowledge
of the objects, or any previously-collected training data. We carry out the
training in planetary exploration conditions, with realistic manipulator
models.
