Unsupervised Domain Adaptation for Robust Imitation Learning under Visual Perturbations

Abstract

Vision-based robot manipulation systems often suffer from performance degradation under domain shifts in visual inputs. While data augmentation is commonly employed in reinforcement learning, its application in imitation learning remains relatively underexplored. Our preliminary experiments indicate that simply incorporating augmentation techniques does not yield effective improvements in imitation learning. To address this challenge, we propose a two-stage learning process. First, we develop an adversarial feature learning framework that leverages data augmentation to enhance robustness against domain shifts. Second, we introduce an unsupervised domain adaptation method that adapts models to target environments using only easily collected image data. In robotic tasks, visual domain shifts can often be detected from initial observations alone. Since collecting complete action-labeled episodes in new domains is expensive, adapting with only initial images greatly reduces data collection costs. To this end, we develop an adaptation strategy that relies solely on initial target-domain observations, eliminating the need for labeled demonstrations. Experimental results across both simulation and physical robot implementations demonstrate that our method preserves source domain performance while exhibiting enhanced resilience to visual perturbations, including varying lighting conditions, background modifications, and environmental distractors.