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

2025-02-05

AI Summary

To address the challenge of extracting robust domain-invariant features from high-dimensional, noisy, and partially observable visual inputs in cross-domain imitation learning, this paper proposes a decoupled temporal modeling framework. First, frame-wise domain-invariant features are extracted via adversarial domain alignment and a self-supervised frame encoder. Second, a temporal attention mechanism constructs dynamic representations, while a novel frame-level temporal annotation technique explicitly decouples visual appearance from behavioral timing. Crucially, the method achieves cross-domain behavioral alignment without reward signals. Evaluated across multiple source visual environments, it improves cross-domain imitation success rates by 23.6% on average, significantly enhancing generalization and robustness to visual noise. This work establishes a new unsupervised paradigm for cross-domain skill transfer.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Computer Vision: Visual Reasoning & Symbolic Representations
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

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.

Problem

• Cross-domain imitation learning challenges
• Domain-invariant feature extraction
• Frame-wise time labeling technique

Innovation

• Domain-invariant per-frame feature extraction
• Frame-wise time labeling technique
• Sequential adaptation of expert behaviors