Rearrangement-Based Manipulation via Kinodynamic Planning and Dynamic Planning Horizons

TL;DR

This paper introduces a dynamic planning framework for robot manipulation in cluttered environments, combining sampling-based planning with adaptive horizons to improve efficiency and robustness in complex rearrangement tasks.

Contribution

It presents a novel framework that interleaves planning and execution with dynamic horizon control, enabling flexible goal definitions and improved handling of uncertainties.

Findings

Enhanced planning efficiency over baseline methods

Increased robustness against physical uncertainties

Higher task success rates within limited time budgets

Abstract

Robot manipulation in cluttered environments often requires complex and sequential rearrangement of multiple objects in order to achieve the desired reconfiguration of the target objects. Due to the sophisticated physical interactions involved in such scenarios, rearrangement-based manipulation is still limited to a small range of tasks and is especially vulnerable to physical uncertainties and perception noise. This paper presents a planning framework that leverages the efficiency of sampling-based planning approaches, and closes the manipulation loop by dynamically controlling the planning horizon. Our approach interleaves planning and execution to progressively approach the manipulation goal while correcting any errors or path deviations along the process. Meanwhile, our framework allows the definition of manipulation goals without requiring explicit goal configurations, enabling the robot to flexibly interact with all objects to facilitate the manipulation of the target ones. With extensive experiments both in simulation and on a real robot, we evaluate our framework on three manipulation tasks in cluttered environments: grasping, relocating, and sorting. In comparison with two baseline approaches, we show that our framework can significantly improve planning efficiency, robustness against physical uncertainties, and task success rate under limited time budgets.