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Updated: Apr 15, 2026

Operation of the Collaborative Composite Manufacturing CCM System
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Policy-Guided Model Predictive Path Integral for Safe Manipulator Trajectory Planning.

Liang Liang1,2, Chengdong Wu3, Xiaofeng Wang2

  • 1College of Information Science and Engineering, Northeastern University, Shenyang 110819, China.

Sensors (Basel, Switzerland)
|April 14, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces a Policy-Guided Model Predictive Path Integral (PG-MPPI) framework for safe manipulator trajectory planning. The PG-MPPI enhances obstacle avoidance and ensures safety in complex environments.

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Safe trajectory planning for manipulators in complex environments faces challenges with hard-constraint enforcement and environmental generalization.
  • Existing methods struggle to balance real-time optimization with robust safety guarantees.

Purpose of the Study:

  • To propose a novel Policy-Guided Model Predictive Path Integral (PG-MPPI) planning framework for manipulator trajectory planning.
  • To improve hard-constraint enforcement and environmental generalization capabilities for safe robot operation.

Main Methods:

  • Developed a Constraint-Discounted Soft Actor-Critic (CD-SAC) offline learning policy incorporating a configuration-space distance field for obstacle avoidance.
  • Integrated the offline policy with Model Predictive Path Integral (MPPI) for guided online sampling and optimization.
Keywords:
configuration-space distance fieldcontrol barrier functionmanipulator trajectory planningmodel predictive path integralreinforcement learning

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  • Implemented a Control Barrier Function (CBF) safety filter for real-time control command revision and constraint satisfaction.

    • Main Results:

    • The PG-MPPI algorithm demonstrated superior performance in collision-free target reaching success rates compared to other algorithms.
    • The proposed method ensures trajectory smoothness and feasibility in multi-obstacle scenarios.
    • The framework exhibits strong adaptive capacity to complex environments with unknown obstacle configurations.

    Conclusions:

    • The PG-MPPI framework offers an efficient and safe solution for autonomous manipulator operation in complex, dynamic environments.
    • The integration of RL and MPC with CBF significantly enhances safety and generalization.
    • This approach advances the state-of-the-art in safe robot motion planning.