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    This study introduces a novel constrained iterative linear quadratic regulator (iLQR) algorithm for trajectory planning. It integrates machine learning to efficiently plan robot movement without needing system identification.

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    Area of Science:

    • Robotics and Autonomous Systems
    • Machine Learning
    • Optimization

    Background:

    • Trajectory planning is crucial for robotics and autonomous systems.
    • Iterative Linear Quadratic Regulator (iLQR) is an effective method for nonlinear dynamics in unconstrained spaces.
    • Existing methods often require system identification and struggle with constraints.

    Purpose of the Study:

    • To present a local-learning-enabled constrained iLQR algorithm for trajectory planning.
    • To develop a method that bypasses the need for explicit system identification.
    • To enable simultaneous refinement of optimal policy and system models within an iterative framework.

    Main Methods:

    • Hybrid dynamic optimization and machine learning approach.
    • Utilizes a neural network to represent local system dynamics.
    • Iterative refinement of policy and neural network system model.
    • Preserves general form constraints typical in trajectory planning.

    Main Results:

    • Demonstrates effective trajectory planning without system identification.
    • Achieves sample-efficient training due to simple neural network architecture.
    • Successfully handles general constraints in trajectory planning tasks.
    • Illustrative examples confirm the algorithm's effectiveness and significance.

    Conclusions:

    • The proposed algorithm offers an efficient and effective solution for constrained trajectory planning.
    • Integrating machine learning with iLQR enhances sample efficiency and removes system identification requirements.
    • This approach advances autonomous systems by enabling robust and adaptable trajectory planning.