Restoring Noisy Demonstration for Imitation Learning With Diffusion Models.

Area of Science:

• Robotics
• Machine Learning
• Artificial Intelligence

Background:

• Imitation learning (IL) enables policy learning from expert demonstrations without requiring environmental interaction or reward signals.
• Existing IL algorithms often assume perfect expert data, which is unrealistic due to human errors or system inaccuracies.
• Noisy expert demonstrations pose a significant challenge for effective policy learning in IL.

Purpose of the Study:

• To develop a robust framework for imitation learning that can effectively handle imperfect expert demonstrations.
• To leverage noisy offline demonstration data by filtering clean samples and restoring corrupted ones.
• To improve the performance and applicability of imitation learning in real-world scenarios with imperfect data.

Main Methods:

• A novel filter-and-restore framework is proposed to address noisy expert demonstrations in imitation learning.
• The framework first identifies and filters clean data samples from the expert demonstrations.
• Conditional diffusion models are then employed to recover and restore the noisy or imperfect data samples.

Main Results:

• The proposed filter-and-restore framework consistently outperformed existing methods across various domains, including robot arm manipulation, dexterous manipulation, and locomotion.
• Ablation studies confirmed the effectiveness of individual components within the framework.
• The framework demonstrated robustness to different types and levels of noise in the demonstration data.

Conclusions:

• The proposed framework offers a practical and effective solution for utilizing noisy offline demonstration data in imitation learning.
• It significantly enhances the performance of imitation learning by robustly handling imperfect expert demonstrations.
• This work advances the applicability of imitation learning in real-world robotic systems where perfect data is scarce.