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

• URL: http://arxiv.org/abs/2502.02867v2
• Date: Fri, 14 Feb 2025 11:57:25 GMT
• Title: Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations
• Authors: Minung Kim, Kawon Lee, Jungmo Kim, Sungho Choi, Seungyul Han,
• Abstract summary: 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.<n>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.
• Score: 5.971046215117033
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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