Domain Adaptive Imitation Learning with Visual Observation
Sungho Choi Seungyul Han Woojun Kim Jongseong Chae Whiyoung Jung Youngchul Sung
In this paper, we consider domain-adaptive imitation learning with visual
observation, where an agent in a target domain learns to perform a task by
observing expert demonstrations in a source domain. Domain adaptive imitation
learning arises in practical scenarios where a robot, receiving visual sensory
data, needs to mimic movements by visually observing other robots from
different angles or observing robots of different shapes. To overcome the
domain shift in cross-domain imitation learning with visual observation, we
propose a novel framework for extracting domain-independent behavioral features
from input observations that can be used to train the learner, based on dual
feature extraction and image reconstruction. Empirical results demonstrate that
our approach outperforms previous algorithms for imitation learning from visual
observation with domain shift.