PlaCo: a QP-based robot planning and control framework

2025-11-08

AI Summary

To address the challenges of complex quadratic programming (QP) modeling, fragmented interfaces, and the trade-off between development efficiency and real-time performance in robot motion planning and control, this paper proposes a hierarchical abstraction framework for QP. The framework introduces a high-level task description language to modularly specify objective functions and diverse constraints—including kinematic, dynamic, and collision-avoidance constraints—automatically translating them into standard QP form. A lightweight, real-time–optimized C++ core ensures low-latency computation, while Python bindings enable rapid prototyping. Crucially, the framework abstracts away low-level mathematical details, enhancing model readability and reusability. Experimental evaluation on representative mobile manipulator tasks demonstrates millisecond-scale QP solution times, seamless deployment, and successful integration across multiple real-world robotic platforms—effectively bridging the gap between algorithmic design and practical implementation.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Knowledge Representation and Reasoning: Qualitative Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This article introduces PlaCo, a software framework designed to simplify the formulation and solution of Quadratic Programming (QP)-based planning and control problems for robotic systems. PlaCo provides a high-level interface that abstracts away the low-level mathematical formulation of QP problems, allowing users to specify tasks and constraints in a modular and intuitive manner. The framework supports both Python bindings for rapid prototyping and a C++ implementation for real-time performance.

Problem

• Simplifying QP-based robot planning and control formulation
• Providing modular interface for task and constraint specification
• Supporting rapid prototyping and real-time robotic performance

Innovation

• QP-based planning and control framework
• High-level interface abstracts mathematical formulation
• Python and C++ for prototyping and real-time