BAGAIL: Multi-modal imitation learning from imbalanced demonstrations.

Area of Science:

• Robotics
• Machine Learning
• Artificial Intelligence

Background:

• Imitation learning methods often struggle with imbalanced expert demonstrations across multiple behavioral modes.
• Existing multi-modal imitation learning techniques typically require balanced data ratios, leading to mode collapse or biased learning when data is imbalanced.

Purpose of the Study:

• To propose a novel algorithm, BAlanced Generative Adversarial Imitation Learning (BAGAIL), capable of learning from imbalanced expert demonstrations in multi-modal imitation learning.
• To address the limitations of existing methods that fail to learn effectively from datasets with varying data distributions per mode.

Main Methods:

• BAGAIL modifies the discriminator's output to balance real-fake and classification losses, ensuring the generator is rewarded for accurate, mode-specific trajectories.
• The algorithm employs a two-stage learning process: pre-training with conditional Behavioral Cloning and imitation learning using a modified discriminator and generator adversarial setup.

Main Results:

• BAGAIL successfully distinguishes between different behavioral modes even with significantly imbalanced expert demonstrations.
• The proposed method demonstrates stable and expert-level learning performance across all modes, outperforming other multi-modal imitation learning approaches.

Conclusions:

• BAGAIL provides an effective solution for multi-modal imitation learning with imbalanced data, overcoming common challenges like mode collapse.
• The algorithm enables robust policy learning that accurately reflects expert behavior across diverse modes, regardless of data distribution disparities.