Diverse Imitation Learning via Self-Organizing Generative Models.

Area of Science:

• Robotics
• Machine Learning
• Artificial Intelligence

Background:

• Imitation learning (IL) aims to replicate expert policies from demonstration trajectories in Markov decision processes (MDPs).
• Existing IL methods struggle with experts exhibiting mixed behavior modes, failing to accurately imitate individual modes.
• Prior work using latent variables for policy variation has shown limitations in multimodal imitation.

Purpose of the Study:

• To develop an improved imitation learning method capable of accurately distinguishing and imitating multiple expert behavior modes.
• To address the limitations of current IL techniques in handling multimodal expert policies.
• To enhance the robustness of learned policies against compounding errors in unseen states.

Main Methods:

• Introduced a self-organizing generative (SOG) model, inspired by self-organizing maps (SOMs), as an encoder-free generative model for multimodal data distributions.
• Applied the SOG model within behavior cloning (BC) to accurately distinguish and imitate different expert modes.
• Integrated the SOG-enhanced BC with generative adversarial IL (GAIL) for robust policy learning in the presence of environmental dynamics.

Main Results:

• The proposed SOG model effectively learns multimodal data distributions from samples.
• The SOG-based approach significantly outperforms state-of-the-art methods in imitation learning tasks.
• Experiments conducted in the MuJoCo simulator demonstrated superior performance in locomotion and robotic manipulation tasks.

Conclusions:

• The self-organizing generative model provides a powerful new approach for imitation learning, particularly for multimodal expert behaviors.
• Integrating SOG with BC and GAIL leads to more accurate and robust policy imitation.
• This method offers significant advancements for developing versatile robots capable of learning from complex demonstrations.