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This study introduces a structured neural network model predictive control (SNN-MPC) for robots. The SNN-MPC effectively predicts contact events in real-time, improving robot manipulation tasks.

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Area of Science:

  • Robotics
  • Machine Learning
  • Control Theory

Background:

  • Model-based control offers high sampling efficiency for robotic systems.
  • Practical robot control, especially precise manipulation, necessitates handling physical contacts and generating accurate motions.
  • Accurate prediction of contact events is crucial for real-time, model-based control in contact-rich tasks.

Purpose of the Study:

  • To investigate the capability of neural network models in learning task-related models for model-based control.
  • To develop a method for predicting future states, including contact events, using neural networks.
  • To address the challenges of contact-rich dynamics in real-time robot control.

Main Methods:

  • Proposed a structured neural network model predictive control (SNN-MPC) method.
  • Designed a neural network architecture with explicit inertia matrix representation.
  • Developed a two-stage modeling procedure for training on contact-rich dynamics using limited samples.
  • Utilized a trackball manipulation task with a 3-DoF finger robot.

Main Results:

  • The SNN-MPC method demonstrated superior performance compared to conventional model predictive control (MPC) with fully connected networks.
  • The proposed method successfully learned a task-related model capable of predicting future states and contact events.
  • Achieved improved outcomes in the challenging trackball manipulation task.

Conclusions:

  • Neural network models can be effectively trained to learn dynamics for model-based control in contact-rich scenarios.
  • The SNN-MPC approach offers a promising solution for real-time control of robots in tasks requiring precise manipulation and contact handling.
  • Explicitly incorporating physical properties like the inertia matrix in neural network design enhances predictive capabilities.