  We optimize a six degrees of freedom hovering policy using reinforcement
meta-learning. The policy maps flash LIDAR measurements directly to on/off
spacecraft body-frame thrust commands, allowing hovering at a fixed position
and attitude in the asteroid body-fixed reference frame. Importantly, the
policy does not require position and velocity estimates, and can operate in
environments with unknown dynamics, and without an asteroid shape model or
navigation aids. Indeed, during optimization the agent is confronted with a new
randomly generated asteroid for each episode, insuring that it does not learn
an asteroid's shape, texture, or environmental dynamics. This allows the
deployed policy to generalize well to novel asteroid characteristics, which we
demonstrate in our experiments. Moreover, our experiments show that the
optimized policy adapts to actuator failure and sensor noise. Although the
policy is optimized using randomly generated synthetic asteroids, it is tested
on two shape models from actual asteroids: Bennu and Itokawa. We find that the
policy generalizes well to these shape models. The hovering controller has the
potential to simplify mission planning by allowing asteroid body-fixed hovering
immediately upon the spacecraft's arrival to an asteroid. This in turn
simplifies shape model generation and allows resource mapping via remote
sensing immediately upon arrival at the target asteroid.
