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

TL;DR

COAD introduces a constant-time planning framework for robotic manipulation tasks with continuous goal spaces, using a compressed library and online adaptation to achieve fast, efficient, and reliable motion planning.

Contribution

The paper presents COAD, a novel approach that combines library compression and online adaptation to enable constant-time planning in continuous task spaces.

Findings

Achieves sub-millisecond query times in simulation and real-world tests.

Maintains high success rates with significantly compressed motion libraries.

Outperforms baseline methods in efficiency and path quality.

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.