  Imitation learning has gained immense popularity because of its high
sample-efficiency. However, in real-world scenarios, where the trajectory
distribution of most of the tasks dynamically shifts, model fitting on
continuously aggregated data alone would be futile. In some cases, the
distribution shifts, so much, that it is difficult for an agent to infer the
new task. We propose a novel algorithm to generalize on any related task by
leveraging prior knowledge on a set of specific tasks, which involves assigning
importance weights to each past demonstration. We show experiments where the
robot is trained from a diversity of environmental tasks and is also able to
adapt to an unseen environment, using few-shot learning. We also developed a
prototype robot system to test our approach on the task of visual navigation,
and experimental results obtained were able to confirm these suppositions.
