COAD: Constant-Time Planning for Continuous Goal Manipulation with Compressed Library and Online Adaptation

2026-03-12

AI Summary

This work addresses the inefficiency of repetitive planning, large solution library storage requirements, and lack of continuous coverage guarantees in parametric manipulation tasks with continuous targets. To overcome these challenges, the authors propose a coverage-based compressed solution library approach: the task space is discretized offline, and only representative root problems are solved to construct a compact library. During online execution, root trajectories are retrieved in constant time and rapidly adapted to target poses through linear interpolation, Dynamic Movement Primitives (DMPs), and lightweight trajectory optimization. This method ensures full coverage of the continuous task space while drastically reducing library size. Experiments in both simulation and real-world settings demonstrate sub-millisecond planning times, high success rates, and superior path quality compared to baseline approaches.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

In many robotic manipulation tasks, the robot repeatedly solves motion-planning problems that differ mainly in the location of the goal object and its associated obstacle, while the surrounding workspace remains fixed. Prior works have shown that leveraging experience and offline computation can accelerate repeated planning queries, but they lack guarantees of covering the continuous task space and require storing large libraries of solutions. In this work, we present COAD, a framework that provides constant-time planning over a continuous goal-parameterized task space. COAD discretizes the continuous task space into finitely many Task Coverage Regions. Instead of planning and storing solutions for every region offline, it constructs a compressed library by only solving representative root problems. Other problems are handled through fast adaptation from these root solutions. At query time, the system retrieves a root motion in constant time and adapts it to the desired goal using lightweight adaptation modules such as linear interpolation, Dynamic Movement Primitives, or simple trajectory optimization. We evaluate the framework on various manipulators and environments in simulation and the real world, showing that COAD achieves substantial compression of the motion library while maintaining high success rates and sub-millisecond-level queries, outperforming baseline methods in both efficiency and path quality. The source code is available at https://github.com/elpis-lab/CoAd.

Problem

• continuous goal manipulation
• motion planning
• task space coverage
• library compression
• constant-time planning

Innovation

• constant-time planning
• compressed motion library
• online adaptation
• task coverage regions
• goal-parameterized manipulation