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Home
Policy-guided Model Predictive Path Integral For Safe Manipulator Trajectory Planning.

Home
Policy-guided Model Predictive Path Integral For Safe Manipulator Trajectory Planning.

Related Experiment Video

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Policy-Guided Model Predictive Path Integral for Safe Manipulator Trajectory Planning.

Liang Liang^1,2, Chengdong Wu³, Xiaofeng Wang²

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

Sensors (Basel, Switzerland)

|April 14, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

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.

Keywords:

configuration-space distance field control barrier function manipulator trajectory planning model predictive path integral reinforcement learning

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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.

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.