Endowing robots with the ability to rearrange various large and heavy objects, such as furniture, can substantially alleviate human workload. However, this task is extremely challenging due to the need to interact with diverse objects and efficiently rearrange multiple objects in complex environments while ensuring collision-free loco-manipulation. In this work, we present ALORE, an autonomous large-object rearrangement system for a legged manipulator that can rearrange various large objects across diverse scenarios. The proposed system is characterized by three main features: (i) a hierarchical reinforcement learning training pipeline for multi-object environment learning, where a high-level object velocity controller is trained on top of a low-level whole-body controller to achieve efficient and stable joint learning across multiple objects; (ii) two key modules, a unified interaction configuration representation and an object velocity estimator, that allow a single policy to regulate planar velocity of diverse objects accurately; and (iii) a task-and-motion planning framework that jointly optimizes object visitation order and object-to-target assignment, improving task efficiency while enabling online replanning. Comparisons against strong baselines show consistent superiority in policy generalization, object-velocity tracking accuracy, and multi-object rearrangement efficiency. Key modules are systematically evaluated, and extensive simulations and real-world experiments are conducted to validate the robustness and effectiveness of the entire system, which successfully completes 8 continuous loops to rearrange 32 chairs over nearly 40 minutes without a single failure, and executes long-distance autonomous rearrangement over an approximately 40 m route. The open-source packages are available at https://zhihaibi.github.io/Alore/.

翻译：赋予机器人重排各种大型重物（如家具）的能力，可显著减轻人类工作负担。然而，由于需与多样物体交互、在复杂环境中高效重排多个物体，并确保无碰撞的移动-操作协同，该任务极具挑战性。本研究提出ALORE，一种面向腿式操纵器的自主大物体重排系统，能够在多样化场景中重排各类大型物体。该系统的核心特征包括：（i）面向多物体环境学习的分层强化学习训练框架，其中在底层全身控制器之上训练高层物体速度控制器，以实现跨多个物体的高效稳定联合学习；（ii）两个关键模块——统一交互配置表示与物体速度估计器，使单一策略能够精确调控多样物体的平面速度；（iii）任务与运动规划框架，联合优化物体访问顺序与物体-目标分配关系，在提升任务效率的同时支持在线重规划。与强基线方法的对比实验表明，本系统在策略泛化性、物体速度跟踪精度及多物体重排效率方面均具有持续优势。通过对关键模块的系统性评估，以及大量仿真与实物实验，验证了整体系统的鲁棒性与有效性：系统成功完成8个连续循环，在近40分钟内无故障地重排了32把椅子，并在约40米的路线上实现了长距离自主重排。开源代码包发布于 https://zhihaibi.github.io/Alore/。