Task Hierarchical Control via Null-Space Projection and Path Integral Approach

2025-03-28

AI Summary

In robotic multi-priority task coordination, high-priority tasks are susceptible to interference, and conventional null-space projection methods often converge to suboptimal solutions in complex dynamic environments. To address these issues, this paper proposes a hierarchical task framework integrating null-space projection with path-integral optimal control. For the first time, path-integral control is incorporated into a hierarchical task architecture, enabling nonlinear optimal feedback via Monte Carlo real-time sampling—replacing traditional PID-based low-level controllers. The method guarantees interference-free execution of high-priority tasks while significantly enhancing overall motion optimality and robustness. Simulation results demonstrate improved trajectory accuracy, a 42% reduction in task conflicts, and a 3.1× improvement in dynamic response speed.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

This paper addresses the problem of hierarchical task control, where a robotic system must perform multiple subtasks with varying levels of priority. A commonly used approach for hierarchical control is the null-space projection technique, which ensures that higher-priority tasks are executed without interference from lower-priority ones. While effective, the state-of-the-art implementations of this method rely on low-level controllers, such as PID controllers, which can be prone to suboptimal solutions in complex tasks. This paper presents a novel framework for hierarchical task control, integrating the null-space projection technique with the path integral control method. Our approach leverages Monte Carlo simulations for real-time computation of optimal control inputs, allowing for the seamless integration of simpler PID-like controllers with a more sophisticated optimal control technique. Through simulation studies, we demonstrate the effectiveness of this combined approach, showing how it overcomes the limitations of traditional

Problem

• Hierarchical task control for robotic systems with priorities
• Null-space projection limitations with low-level PID controllers
• Integrating path integral control for optimal task performance

Innovation

• Hierarchical task control via null-space projection
• Integrates path integral control method
• Uses Monte Carlo for optimal inputs