Diffusion Model-Based Path Follower for a Salamander-Like Robot.

Area of Science:

• Robotics
• Artificial Intelligence
• Control Systems

Background:

• Salamander-like robots exhibit complex locomotion, posing challenges for traditional path-following controllers.
• Existing learning-based methods struggle with data efficiency, prediction, and reward function design.

Purpose of the Study:

• To develop an advanced control framework for biomimetic robots, specifically salamander-like robots.
• To overcome limitations of conventional and existing learning-based controllers in path following.

Main Methods:

• Proposed a diffusion model-based hierarchical control framework treating path tracking as sequence generation.
• Integrated a high-level guided diffusion policy with a low-level inverse kinematics controller.
• Optimized the diffusion model with lightweight architectures and attention mechanisms for resource-constrained deployment.

Main Results:

• The diffusion model framework demonstrated superior data utilization, stable training, and long-horizon dependency modeling.
• Achieved efficient learning by reducing the action space and improving state-action-reward sequence modeling.
• Framework proved effective, efficient, and robust in simulations and real-world experiments.

Conclusions:

• The proposed diffusion model-based hierarchical control offers a significant advancement for controlling salamander-like robots.
• This approach enhances path-following capabilities in biomimetic robots, addressing previous control challenges.
• The framework's optimization ensures practical applicability on physical robots with limited computational power.