A physics-guided neural network framework for prediction and control of spring-mass running

Area of Science:

• Robotics and Biomechanics
• Machine Learning for Control Systems

Background:

• The spring-mass model is crucial for understanding animal locomotion and designing legged robots.
• Controlling spring-mass dynamics faces a trade-off between accurate but computationally expensive numerical integration and efficient but error-prone analytical approximations.

Purpose of the Study:

• To develop a physics-guided hybrid neural network framework for the spring-mass running template.
• To achieve accurate and computationally efficient control of legged robotic platforms.

Main Methods:

• A hybrid neural network architecture embedding exact analytical solutions for flight dynamics and learning non-integrable stance dynamics.
• A mixed-objective training strategy combining supervised imitation and goal-conditioned learning.
• Extensive simulations, Basin of Attraction (BoA) analysis, and validation against experimental data from a one-legged hopper.

Main Results:

• The proposed framework achieves high prediction accuracy and control precision with low runtime efficiency.
• The analytically augmented design enhances interpretability without sacrificing performance.
• Basin of Attraction analysis demonstrates superior stability and robustness against perturbed initial conditions compared to existing methods.
• Validation against experimental data confirms real-world adaptability.

Conclusions:

• The hybrid physics-guided neural network offers a superior approach to controlling spring-mass dynamics for legged robots.
• This method overcomes the limitations of traditional numerical and analytical techniques, enabling real-time embedded control.
• The framework demonstrates significant improvements in accuracy, robustness, and efficiency for robotic locomotion.