  Learning from demonstrations is a useful way to transfer a skill from one
agent to another. While most imitation learning methods aim to mimic an expert
skill by following the demonstration step-by-step, imitating every step in the
demonstration often becomes infeasible when the learner and its environment are
different from the demonstration. In this paper, we propose a method that can
imitate a demonstration composed solely of observations, which may not be
reproducible with the current agent. Our method, dubbed selective imitation
learning from observations (SILO), selects reachable states in the
demonstration and learns how to reach the selected states. Our experiments on
both simulated and real robot environments show that our method reliably
performs a new task by following a demonstration. Videos and code are available
at https://clvrai.com/silo .
