Embodied Scene Rearrangement Planning
Proposes ESRP task with egocentric observations and top-down layouts; introduces ESRP-Bench with 5400+ scene pairs; baseline success only 30.2%.
Key Findings
Methodology
The ESRP task challenges embodied agents to rearrange furniture in 3D scenes using only egocentric views and a top-down target layout, without access to global state or ground-truth localization. The authors develop ESRP-Bench, a large-scale benchmark based on OmniGibson, with over 5400 scene pairs and 8200 objects across diverse room types. They design three evaluation metrics: success rate, object success rate, and remaining distance ratio. Four baselines are implemented: imitation learning (IL), reinforcement learning (RL) with PPO, a vision-language model (VLM) prompted in a ReAct framework, and a hierarchical task and motion planning (TAMP) approach that uses privileged global information. Experiments reveal significant challenges, with the best success rate only 30.2%, especially in complex scenarios.
Key Results
- The top-performing model achieves only 30.2% success, with performance dropping to near zero in complex scenes involving multiple objects and occlusions.
- IL and RL models reach about 70% in object placement accuracy but struggle with efficiency and robustness.
- TAMP performs well under ideal conditions but is infeasible in real-world noisy environments, highlighting the need for better perception and planning under partial observability.
Significance
This work advances embodied AI by formalizing a realistic furniture rearrangement task that emphasizes partial observations and long-term planning. It bridges the gap between simplified simulation and real-world deployment, fostering research in scene understanding, perception, and decision-making for autonomous agents in complex indoor environments. The ESRP-Bench provides a comprehensive platform for benchmarking progress in this domain.
Technical Contribution
The paper introduces a novel long-horizon, partial-observation rearrangement task, constructs a large-scale benchmark dataset, and develops multiple baseline approaches integrating deep learning, multimodal reasoning, and hierarchical planning. It demonstrates the limitations of current methods and sets a new challenge for future research in embodied AI and robotics.
Novelty
This is the first work to define a furniture rearrangement task in fully 3D scenes with egocentric perception and partial observability, combining multimodal models and hierarchical planning to address real-world constraints, thus pushing the frontier of embodied scene understanding.
Limitations
- Current models exhibit low success rates in highly occluded and cluttered environments, indicating the need for more robust perception and reasoning modules.
- Simulation-based experiments may not fully capture real-world uncertainties like sensor noise and hardware limitations, requiring transfer learning and real robot validation.
- Task complexity is limited to scenes with up to six objects; scaling to larger environments remains an open challenge.
Future Work
Future directions include integrating more advanced perception modules, such as depth sensing and semantic segmentation, to improve scene understanding under occlusion. Developing more scalable planning algorithms and transferring models from simulation to real robots are also key goals. Additionally, expanding the dataset to include more diverse environments and dynamic scene changes will further enhance system robustness.
AI Executive Summary
Robotic scene rearrangement has long been a key challenge in embodied AI, yet most existing solutions rely heavily on full environment maps and precise localization, limiting their applicability in real-world scenarios. In practical settings like homes or offices, robots must operate under partial observations, with furniture often occluded and environments constantly changing. Addressing this gap, the authors introduce Embodied Scene Rearrangement Planning (ESRP), a novel task that simulates a robot rearranging furniture in 3D scenes using only egocentric views and a top-down target layout.
This task captures the core difficulties faced by real robots: partial observability, mutual occlusion, and long-horizon planning. To facilitate research, the team built ESRP-Bench, a comprehensive benchmark based on OmniGibson, featuring over 5400 scene pairs and more than 8200 objects across various room types. They designed three evaluation metrics—success rate, object success rate, and remaining distance ratio—to assess the quality of rearrangement. Four baseline approaches were implemented: IL, RL with PPO, a vision-language model prompted in a ReAct framework, and a hierarchical task and motion planner that uses privileged global information.
Experimental results highlight the significant challenges of ESRP. The best success rate achieved by current methods is only 30.2%, with performance plummeting in complex scenes involving multiple objects and occlusions. While IL and RL models perform reasonably well in object placement, they lack robustness and efficiency. The TAMP approach excels under ideal conditions but fails in noisy, partial observation environments. These findings underscore the need for more advanced perception, reasoning, and planning techniques.
This research marks a critical step toward deploying autonomous agents capable of understanding and manipulating complex indoor environments under realistic constraints. By establishing a challenging benchmark and demonstrating current limitations, it sets a clear agenda for future work in perception-driven long-term planning, multimodal reasoning, and real-world robotic deployment. Ultimately, ESRP paves the way for robots that can autonomously organize and adapt in dynamic, cluttered spaces, bringing embodied AI closer to practical applications.
Deep Analysis
Background
场景重排作为机器人自主操作的核心任务之一，经历了从简单的桌面对象操作到复杂的室内环境布局调整的演变。早期研究如GraspNet和Gibson环境主要关注单一物体的抓取与放置，逐步引入多目标协调和路径规划技术。近年来，深度强化学习（如DQN、DDPG）和基于规则的任务规划方法被广泛应用，但多依赖全局地图和精确定位，难以应对遮挡、部分观察和动态环境的实际挑战。多模态感知（视觉、语言）结合层次化规划逐渐成为研究热点，但在真实复杂场景中的系统验证仍不足。本文在此基础上，提出了在有限观察和遮挡条件下，利用多模态信息实现家具长远重排的创新任务，推动了 embodied AI 在实际应用中的发展。
Core Problem
核心问题在于如何在缺乏全局状态信息、存在遮挡和部分观察的限制下，实现家具的长远重排。传统方法依赖全局地图和精确定位，难以适应实际场景中的感知误差和遮挡干扰。长远规划涉及多步骤、多目标的连续决策，要求模型具备强大的空间推理和目标管理能力。在复杂环境中，遮挡、多目标协调和动态变化使得任务难度大幅提升，现有技术难以满足高效、鲁棒的自主操作需求。这些限制成为推动该研究的重要动力。
Innovation
本研究的创新点包括：1）提出基于egocentric观察的家具长远重排任务，模拟真实机器人操作场景；2）构建规模庞大的ESRP-Bench，涵盖多样房型和家具类别，提供多层次评估体系；3）融合层次化任务规划与运动规划，结合深度学习模型（IL、RL、VLM）提升感知理解和决策能力；4）引入多指标评价体系，全面衡量模型在复杂环境中的表现。这些创新突破了传统依赖全局信息的限制，为自主机器人在复杂场景中的操作提供了新思路。
Methodology
- �� 任务定义：在无全局状态信息、存在遮挡的3D场景中，利用第一人称观察和目标布局图，完成家具重排。
- �� 输入：每个时间步获得egocentric RGB图像、目标布局图和抓取状态指示。
- �� 方法框架：
- 采用层次化任务与运动规划（TAMP）结合深度学习模型，包括目标检测、空间推理、任务规划和运动路径生成。
- IL模型通过专家轨迹学习行为模仿，RL模型采用PPO优化策略，VLM模型在ReAct框架中进行推理。
- 结合多模态信息，提升场景理解和决策能力。
- �� 训练：利用专家轨迹进行模仿学习，结合强化学习优化策略，训练多模型。
- �� 评价：采用成功率、目标成功率和剩余距离比等指标，全面评估模型性能。
Experiments
在OmniGibson模拟环境中，构建5400余场景对，涵盖多样房型和家具。设计IL、RL、VLM和TAMP四个基线模型，比较不同方法在不同难度场景中的表现。训练参数包括：RL采用PPO，学习率1.5×10^-4；IL采用扩散模型，训练50轮，学习率1×10^-4。评估指标包括成功率、目标成功率和剩余距离比。进行不同场景复杂度的消融分析，验证模型在遮挡、多目标协调中的表现。
Results
实验显示，最优模型成功率仅30.2%，在复杂场景中几乎无法完成任务。IL和RL模型在目标放置准确率达70%以上，但整体效率和鲁棒性不足。VLM模型在推理复杂场景时表现有限，TAMP在理想信息下表现较好但不适应遮挡环境。这些数据反映出模型在感知、规划和长远决策方面的不足，强调未来需结合更强的感知推理能力。
Applications
该研究为家庭、仓储等场景中的自主家具重排提供技术基础，适用于智能家居机器人、仓储管理等。未来结合深度感知和多模态技术，提升系统鲁棒性和实际部署能力。长远目标是实现更智能、更自主的场景理解与操作，推动机器人在复杂环境中的广泛应用。
Limitations & Outlook
模型在遮挡、多目标和动态环境中表现仍有限，成功率不足50%。模拟环境虽逼真，但未充分考虑硬件感知误差和运动控制不确定性。任务规模有限，未来需扩展到更大空间和多样场景，提升泛化能力。
Plain Language Accessible to non-experts
想象你在厨房里整理东西，但你只能看到自己面前的一小块空间，不能看到整个厨房。你需要把所有锅碗瓢盆放到正确的位置，但你不知道每个物品的确切位置，只能通过观察自己看到的部分，慢慢推理下一步怎么做。这就像机器人在房间里工作，只能用第一人称视角，没有全局地图，也会被家具挡住视线。它必须不断观察、思考，然后行动，把家具搬到目标位置。这很难，因为每次搬家具都可能遮挡视线，或者需要长时间的规划。研究人员设计了虚拟环境，让机器人学习如何在这些限制下完成家具重排，推动机器人自主操作的发展。
ELI14 Explained like you're 14
想象你在一个房间里玩拼图游戏，但你只能看到自己面前的一小块区域，不能看到整个房间的布局。你需要把所有的拼图片都放到正确的位置上，但你不知道每个拼图片的确切位置，只能通过观察自己看到的部分，慢慢推理出下一步该怎么做。这就像机器人在房间里工作，只能用眼睛看一小部分，不能依赖全局地图，也会被家具挡住视线。它必须不断观察、思考，然后行动，把家具搬到正确的位置。这很难，因为每次搬家具都可能遮挡视线，或者需要很长时间的计划。研究人员设计了虚拟环境，让机器人学习如何在这些限制下完成家具的重排。这项工作帮助机器人变得更聪明、更自主，能在家里帮忙整理房间。
Glossary
Embodied AI (具身人工智能)
指具有物理形态的智能体，能感知和操作环境，区别于纯虚拟模型。
本文中的机器人作为具身AI，需自主感知和操作家具。
Long-horizon Planning (长远规划)
涉及多步骤、多目标的复杂决策过程，要求系统在长时间尺度上做出合理安排。
ESRP任务要求机器人进行长远规划以完成家具重排。
Egocentric Observation (第一人称观察)
由智能体自身视角获得的感知信息，缺乏全局视野。
机器人仅用第一人称观察进行场景理解。
Hierarchical Task and Motion Planning (层次化任务与运动规划)
结合高层任务决策与低层运动控制的规划方法，解决复杂长远任务。
本文采用TAMP框架实现家具重排。
VLM (Vision-Language Model, 视觉-语言模型)
结合视觉和文本信息的多模态模型，用于理解和推理复杂场景。
VLM在ESRP中辅助机器人理解目标布局。
Open Questions Unanswered questions from this research
- 1 如何将模拟训练迁移到真实机器人，确保感知和操作的鲁棒性。
- 2 多目标、多遮挡场景下的长远规划算法如何提升效率。
- 3 硬件感知误差对自主家具重排的影响及缓解策略。
Applications
Immediate Applications
智能家居机器人
实现自主家具重排，提升家庭自动化水平，减少人工干预，适应多样布局变化。
仓储自动化
在仓库中自主整理货架和物品，提高效率，降低人力成本。
Long-term Vision
自主场景理解系统
打造具备复杂场景理解和长远规划能力的通用机器人，广泛应用于家庭、商业和公共空间。
Abstract
This paper introduces Embodied Scene Rearrangement Planning (ESRP), a novel task requiring embodied agents to rearrange furniture in 3D scenes to match a target configuration using only egocentric observations and a top-down target layout. Unlike prior rearrangement tasks, ESRP precludes global state access and introduces mutual object occlusions, reflecting the practical constraints of real-world robotic deployment. These factors make aligning partial egocentric observations with the global target layout particularly challenging for long-horizon planning. To facilitate research, we present ESRP-Bench, a comprehensive benchmark built on OmniGibson featuring over 5,400 scene pairs and 8,200 objects. We define three multi-level metrics to evaluate rearrangement quality and provide four baselines: a hierarchical task-and-motion planning method, a vision-language-model-based method, and two learning-based approaches (IL and RL). Experimental results demonstrate that current methods struggle to complete the task efficiently, highlighting ESRP as a challenging frontier for embodied agents in scene understanding and long-horizon task planning. This work serves as a stepping stone toward deploying intelligent agents in real-world scenarios. Project page: https://pie-lab.cn/ESRP/.