Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations
This addresses challenges in imitation learning for agents using visual data, though it appears incremental as it builds on existing feature extraction and sequence adaptation techniques.
The paper tackled the problem of cross-domain imitation learning with visual observations by proposing DIFF-IL, a method that extracts domain-invariant features from frames and adapts them into sequences, resulting in improved performance across diverse visual environments.
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.