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arXiv · 2502.02867

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations

Minung Kim·Kawon Lee·Jungmo Kim·Sungho Choi·Seungyul Han
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Abstract

Imitation learning (IL) enables agents to mimic expert behavior without reward signals but faces challenges in cross-domain scenarios with high-dimensional, noisy, and incomplete visual observations. To address this, we propose Domain-Invariant Per-Frame Feature Extraction for Imitation Learning (DIFF-IL), a novel IL method that extracts domain-invariant features from individual frames and adapts them into sequences to isolate and replicate expert behaviors. We also introduce a frame-wise time labeling technique to segment expert behaviors by timesteps and assign rewards aligned with temporal contexts, enhancing task performance. Experiments across diverse visual environments demonstrate the effectiveness of DIFF-IL in addressing complex visual tasks.

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Minung Kim, Kawon Lee, Jungmo Kim, Sungho Choi, Seungyul Han. 2025-02-05. Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations. https://arxiv.org/abs/2502.02867

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