GALC: Guided Amplified Learning With Lipschitz Constraint for Robust Trajectory Generation.

Area of Science:

• Robotics
• Machine Learning
• Artificial Intelligence

Background:

• Offline reinforcement learning (RL) shows promise but relies on labor-intensive data collection, especially for humanoid locomotion.
• Existing data augmentation techniques for RL datasets are often noise-sensitive and struggle in complex robotic environments.

Purpose of the Study:

• To develop a novel trajectory augmentation method for offline RL that is noise-insensitive and enhances policy performance.
• To address the limitations of current data augmentation methods in complex robotic tasks.

Main Methods:

• Proposed Guided Amplified Learning with Lipschitz constraint (GALC), a method using a reward-amplification-guided conditional diffusion model.
• Introduced a local Lipschitz continuity constraint to regulate the diffusion model's denoising process, restricting exploration to the dataset's continuity region.
• Ensured generated trajectories are noise-insensitive and prevent unsafe actions.

Main Results:

• GALC successfully generated high-reward trajectories that are robust to perturbations.
• The method prevented the generation of unsafe actions inconsistent with environment dynamics.
• Extensive experiments demonstrated significant improvements in augmented trajectories and policy performance on sparse reward and high-dimensional robotic tasks.

Conclusions:

• GALC offers a robust and effective solution for data augmentation in offline RL, particularly for challenging robotic applications.
• The proposed Lipschitz constraint effectively guides the diffusion model for generating high-quality, safe, and noise-insensitive trajectories.