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mppi-controller

Tune MPPI-based Nav2 control parameters for stable, safe trajectories.

18|2|Updated Apr 20, 2026
Search Tags:#ros2#critic#motion-control#path-planning#nav2#mppi#trajectory-optimization
One-click install
npx skills add https://github.com/wimblerobotics/ros2-copilot-skills --skill mppi-controller
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mppi-controller
Source: https://github.com/wimblerobotics/ros2-copilot-skills/tree/main/mppi-controller
Command: npx skills add https://github.com/wimblerobotics/ros2-copilot-skills --skill mppi-controller

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

MPPI Controller tuning enables ROS 2 navigation practitioners to configure a model predictive path integral controller for Nav2, aligning trajectory rollouts, time horizons, and velocity limits with real robot dynamics.

Core Features & Use Cases

  • Configurable horizon and sampling: adjust time_steps, model_dt, batch_size, and iteration_count to trade off planning quality and CPU usage.
  • Critic-based cost shaping: integrate CostCritic, GoalCritic, PathFollowCritic, and others to balance safety, efficiency, and goal attainment.
  • Production-ready tuning workflow: apply validated defaults and guidelines to tune controllers for indoor navigation, outdoor paths, or highly dynamic environments.

Quick Start

Load a baseline YAML, set time_steps and model_dt to your robot's dynamics, and initialize the MPPI controller to observe trajectory performance.

Frequently Asked Questions about mppi-controller

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I tune MPPI controller parameters in Nav2 for stable trajectory generation?▼

Tuning MPPI controller parameters in Nav2 involves adjusting time_steps, model_dt, batch_size, and iteration_count to align trajectory rollouts with your robot's velocity limits and real dynamics.

What critics are available for cost shaping in a ROS 2 MPPI controller?▼

Cost shaping in a ROS 2 MPPI controller uses CostCritic, GoalCritic, and PathFollowCritic to balance obstacle avoidance, efficiency, and goal attainment during trajectory optimization.

How does adjusting model_dt and time_steps affect Nav2 MPPI planning quality?▼

Adjusting model_dt and time_steps affects MPPI planning quality by changing the prediction horizon, allowing you to trade off trajectory optimization performance against CPU usage.

Can I apply MPPI controller tuning for outdoor navigation and dynamic environments in ROS 2?▼

MPPI controller tuning applies to outdoor navigation and dynamic environments in ROS 2 by configuring validated defaults and critic weights for safe, stable path following.

What are the limitations of using MPPI trajectory optimization in Nav2?▼

MPPI trajectory optimization in Nav2 is limited by CPU constraints from high batch_size and iteration_count, requiring parameter validation and boundary handling to maintain safe defaults.

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