Weakly supervised multi-modal imitation learning from incompletely labeled demonstrations.

Area of Science:

• Robotics
• Artificial Intelligence
• Machine Learning

Background:

• Multi-modal imitation learning allows agents to learn diverse behaviors simultaneously.
• Current methods struggle with incomplete or missing labels in expert demonstrations, leading to inefficiency.

Purpose of the Study:

• To develop an efficient imitation learning approach for multi-modal tasks using incompletely labeled expert data.
• Introduce Weakly Supervised Multi-modal Imitation Learning (WSMIL) to address data labeling challenges.

Main Methods:

• WSMIL integrates weakly supervised learning into a three-player adversarial network (generator, classifier, discriminator).
• It utilizes both labeled and unlabeled data, employing fake state-action-label pairs to train the discriminator.
• Additional losses and simulated annealing behavioral cloning enhance policy generalization and data distribution convergence.

Main Results:

• WSMIL accurately identifies behavior modes even with incomplete labels.
• The approach learns policies that closely match expert performance across different modes.
• WSMIL demonstrates improved stability compared to existing multi-modal imitation learning methods.

Conclusions:

• WSMIL offers an effective solution for imitation learning from incompletely labeled multi-modal demonstrations.
• The method enhances agent learning efficiency and performance in complex, multi-modal tasks.