  Repertoire-based learning is a data-efficient adaptation approach based on a
two-step process in which (1) a large and diverse set of policies is learned in
simulation, and (2) a planning or learning algorithm chooses the most
appropriate policies according to the current situation (e.g., a damaged robot,
a new object, etc.). In this paper, we relax the assumption of previous works
that a single repertoire is enough for adaptation. Instead, we generate
repertoires for many different situations (e.g., with a missing leg, on
different floors, etc.) and let our algorithm selects the most useful prior.
Our main contribution is an algorithm, APROL (Adaptive Prior selection for
Repertoire-based Online Learning) to plan the next action by incorporating
these priors when the robot has no information about the current situation. We
evaluate APROL on two simulated tasks: (1) pushing unknown objects of various
shapes and sizes with a robotic arm and (2) a goal reaching task with a damaged
hexapod robot. We compare with "Reset-free Trial and Error" (RTE) and various
single repertoire-based baselines. The results show that APROL solves both the
tasks in less interaction time than the baselines. Additionally, we demonstrate
APROL on a real, damaged hexapod that quickly learns to pick compensatory
policies to reach a goal by avoiding obstacles in the path.
