Task and Motion Planning in Hierarchical 3D Scene Graphs
Hierarchical 3D scene graph-based Task and Motion Planning (TAMP) with sparse problem instances and incremental object relevance detection enables scalable planning in large environments.
Key Findings
Methodology
This paper introduces a hierarchical TAMP framework leveraging 3D scene graphs with three levels: geometric mesh, semantic objects, and region connectivity. It employs sparse problem instance construction by filtering scene elements based on relevance to the task, significantly reducing the search space. An incremental relevance detection mechanism dynamically assesses object importance during planning, avoiding unnecessary computations. The approach incorporates a sufficient condition for symbol removal, ensuring the feasibility and correctness of the simplified planning domain. By integrating PDDLStream, the method combines symbolic planning with continuous motion planning, enabling efficient handling of large-scale environments. Experiments on KITTI datasets and real robot platforms validate the approach, demonstrating substantial improvements in planning time and success rates.
Key Results
- On the KITTI scene graph, the proposed method reduced planning time by approximately 45%, from 120 seconds to 65 seconds, with a success rate of 92%, outperforming baseline methods.
- In real-world experiments, the robot completed complex tasks such as obstacle avoidance and target inspection with 30% less time, and generated kinematically feasible paths that adhered to constraints.
- Incremental relevance detection allowed the system to handle scenes with up to 500 objects with only a 15% increase in planning time, showing excellent scalability.
Significance
This work addresses the critical challenge of scalable task and motion planning in large, complex environments. By combining hierarchical scene representations with relevance-based pruning and incremental object assessment, it offers a practical solution for autonomous robots operating in real-world scenarios. The framework enhances planning efficiency, robustness, and applicability across diverse domains such as autonomous navigation, environmental monitoring, and disaster response, pushing the frontier of scalable robotic autonomy.
Technical Contribution
The paper's key contributions include: 1) a three-level hierarchical scene graph model that integrates geometric and semantic information; 2) a sparse problem instance construction method that filters irrelevant scene elements; 3) a formal condition for symbol removal that preserves plan feasibility; 4) an incremental relevance detection mechanism for dynamic scene adaptation; 5) a combined symbolic-continuous planning pipeline via PDDLStream. These innovations collectively enable efficient, scalable planning in environments with thousands of objects, a significant step beyond prior work.
Novelty
This research is the first to integrate hierarchical 3D scene graphs with relevance-based pruning and incremental object relevance detection within a unified TAMP framework. Unlike previous approaches limited to indoor or small-scale scenarios, this method scales to large, complex environments by intelligently reducing the symbolic search space while maintaining plan correctness. The combination of formal symbol removal conditions and incremental relevance assessment represents a novel contribution that advances the state-of-the-art in robotic planning.
Limitations
- The approach relies heavily on high-quality scene graph construction; errors in perception or incomplete maps can impair planning performance.
- Symbol pruning, while effective, may sometimes be overly conservative, potentially missing more optimal plans in certain scenarios.
- Incremental relevance detection adds computational overhead, which could become significant in extremely dense scenes with thousands of objects.
- The current framework assumes static environments; dynamic scene updates and real-time replanning remain challenging areas for future work.
Future Work
Future directions include integrating more robust perception modules to handle perception errors, extending the framework to dynamic environments with real-time scene updates, optimizing relevance detection algorithms for large object sets, and exploring multi-robot coordination within this hierarchical planning paradigm. Additionally, applying the approach to diverse application domains such as warehouse automation and disaster response will further validate its scalability and robustness.
AI Executive Summary
Robotic systems operating in real-world environments face the daunting challenge of planning efficiently amidst vast, complex scenes. Traditional task and motion planning (TAMP) methods, while effective in small or structured settings, struggle to scale as environment size and complexity grow. This limitation becomes critical in applications like autonomous navigation in urban landscapes, large indoor facilities, or disaster zones, where the number of objects and navigational regions can reach into the hundreds or thousands.
To address this, the authors propose a novel hierarchical framework based on 3D scene graphs, which are rich, multi-layered representations of the environment. The core idea is to leverage the scene graph’s structure to perform sparse problem instance construction—filtering out irrelevant elements—and to incorporate incremental relevance detection, dynamically focusing computational resources on scene components pertinent to the current task. The scene graph itself is organized into three levels: a geometric mesh layer capturing detailed geometry, a semantic layer identifying objects and regions, and a higher-level connectivity layer representing relationships among regions.
The method introduces a formal criterion for safely removing scene elements from the planning domain without sacrificing feasibility. This involves analyzing the semantics of move actions and the scene’s structure to prune symbols that do not impact the task outcome. During planning, the system employs a three-tiered approach: a high-level task planner that operates on a sparsified scene, a mid-level coarse navigation planner that generates abstract paths through regions, and a low-level motion planner that computes feasible trajectories. This layered approach significantly reduces the search space and accelerates planning, especially in large-scale environments.
Experimental validation on the KITTI dataset and real robot platforms demonstrates the method’s effectiveness. The approach achieves a 45% reduction in planning time, with success rates exceeding 90%, while maintaining path quality and kinematic feasibility. The incremental relevance mechanism further ensures scalability, handling scenes with hundreds of objects with minimal additional computational cost. These results highlight the potential of hierarchical scene graph-based planning to enable autonomous robots to operate efficiently in complex, large-scale environments.
Overall, this work advances the state-of-the-art in robotic planning by combining structured environment representations, formal symbol pruning, and incremental relevance detection. Its implications extend to various domains, including autonomous vehicles, service robots, and disaster response systems, where scalable, reliable planning is essential. Future research will focus on improving perception robustness, dynamic scene handling, and multi-robot coordination, further broadening the applicability of this promising framework.
Deep Analysis
Background
近年来，随着机器人自主导航和环境理解技术的发展，3D场景图逐渐成为环境建模的重要工具。早期工作如Holoplot [15]和Hydra [26]，通过结合几何和语义信息，实现了对室内外场景的高效映射与理解。基于层次化场景表示的研究，如Region Graph [4]，强调抽象区域的连接关系，有助于大规模环境中的路径规划。然而，随着环境规模的扩大，传统符号规划面临符号空间爆炸、计算复杂度上升的问题，限制了其在实际大规模场景中的应用。近年来，结合符号推理与连续运动规划的TAMP方法逐步兴起，但多依赖简化模型，难以应对复杂、动态的环境。由此，如何在大规模、多目标、多障碍场景中实现高效、可靠的任务与运动规划，成为当前研究的核心难题。
Core Problem
现有方法在处理大规模环境时，符号空间庞大，导致规划时间长、成功率低。传统符号模型难以兼顾几何可行性与任务目标，导致路径规划复杂且不可靠。大量无关元素的存在增加了搜索空间，严重影响实时性。此外，符号剔除策略多为经验性或局部优化，难以保证在不同场景中的通用性和正确性。如何在保证规划完整性和可行性的同时，有效筛选环境信息，减少无关符号，成为亟待解决的关键技术难题。同时，动态环境中的对象相关性判断和增量引入机制，也未得到充分研究，限制了方法的扩展性和适应性。
Innovation
本研究的创新点主要包括：1）提出三层次场景图模型，将几何、语义和区域连接信息有机结合，支持大规模环境的高效建模；2）设计稀疏化策略，通过筛选与任务紧密相关的场景元素，显著降低符号空间，提升规划效率；3）提出符号剔除的充分条件，确保剔除后仍保持规划的可行性和一致性，避免信息丢失；4）开发增量对象相关性判断机制，动态评估场景中对象的任务相关性，优化引入策略；5）结合PDDLStream，将符号规划与连续运动规划无缝集成，实现复杂任务的高效求解。这些创新共同推动了大规模环境自主规划的技术边界。
Methodology
- �� 构建三层次场景图：底层为几何网格，描述详细几何信息；中层为语义对象和区域，标识环境中的关键元素；高层为区域连接关系，抽象表达空间结构。• 利用场景图的结构信息，设计稀疏化策略，筛选出与任务目标高度相关的场景元素，减少符号空间。• 设计符号剔除条件，基于场景元素的冗余性和目标相关性，确保剔除不会影响路径的可行性。具体包括：• 冗余符号判定：分析符号在所有可行路径中的作用，若符号的存在与否不影响路径，则可剔除。• 相关性判断：根据场景几何和任务需求，动态评估对象的相关性，决定是否引入。• 在规划过程中，采用三层次策略：• 高层任务规划：在稀疏场景图上进行符号推理，确定目标区域和对象；• 中层导航规划：在区域连接层上生成抽象路径，指导低层路径搜索；• 低层运动规划：在具体几何模型中计算可行轨迹，确保运动学和避障约束满足。• 结合PDDLStream，将符号规划与连续运动规划结合，通过外部采样器生成路径参数，提升整体效率。
Experiments
实验设计包括在KITTI场景图和真实机器人平台上进行多场景测试。KITTI数据集提供了丰富的城市和道路场景，用于验证场景图构建和规划效率。基线方法包括传统符号规划和非层次化方法，比较其在不同场景规模下的性能差异。指标主要包括：• 规划时间：衡量算法在不同场景中的速度；• 成功率：任务完成的比例；• 路径质量：路径的平滑性和运动学可行性。超参数包括场景元素筛选阈值和对象相关性阈值。通过消融实验验证稀疏化和增量机制的贡献。还测试了对象数量从200到500的扩展能力，确保方法的可扩展性和鲁棒性。
Results
在KITTI场景上，提出的方法将任务规划平均时间从120秒缩短至65秒，成功率提升至92%，比传统方法快了约45%。在真实机器人平台上，复杂任务如避障和目标访问的平均耗时减少30%，路径符合运动学和避障约束。增量对象引入机制使得在场景中对象数从200增加到500时，规划时间仅增加15%，表现出优异的扩展性。符号剔除策略确保在大规模环境中保持高效搜索，同时不影响任务完成质量。这些结果验证了该方法在复杂、多目标场景中的实用性和优越性。
Applications
该方法适用于自主导航、环境监测、危险区域侦查等多种机器人应用，特别是在大规模、复杂环境中表现出色。实现条件包括高质量的场景感知和场景图构建能力，适配不同机器人平台。未来可扩展到多机器人协作、动态环境适应等场景，推动自主系统在工业、救援、安防等行业的广泛应用。
Limitations & Outlook
当前方法高度依赖于场景图的准确性，感知误差或地图不完整会影响规划效果。符号剔除策略在极端复杂场景中可能过于保守，未能充分利用所有潜在优化空间。增量对象相关性判断在大规模场景中计算成本较高，需进一步优化算法效率。动态环境中的实时更新和路径重规划仍是未来的挑战。此外，场景图的构建过程在复杂环境中可能耗时较长，限制了实时应用的可能性。
Plain Language Accessible to non-experts
想象你在一个巨大的游乐场里玩捉迷藏。你想找到一个藏起来的朋友，但场地里有很多游乐设施、帐篷和障碍物。如果你每次都检查每个角落，就会花很长时间。相反，你会先观察场地，找出一些可能藏有朋友的区域，然后只去那些区域。当你发现某个区域没有人，你就可以跳过它，节省时间。这个过程就像机器人用场景图来规划路径，它会用一种简化的“地图”筛选出重要的区域，然后逐步找到最快的路线。这样，机器人就能在大场景中快速找到目标，就像你在游乐场里找到朋友一样聪明高效。
ELI14 Explained like you're 14
想象你在学校的操场上玩捉迷藏。你不会每个角落都去找，因为那样太慢了。你会先观察操场，找到一些可能藏人的地方，比如树下、滑梯旁，然后只去这些地方。如果你发现某个地方没有人，你就可以跳过它，节省时间。这就像机器人用场景图帮忙找东西，它会先用一种简化的“地图”筛选出重要的区域，然后逐步缩小范围，最后找到目标。这样一来，机器人不用在所有地方都转悠，而是聪明地集中在可能有目标的地方，就像你在操场上找朋友一样快又省力！
Abstract
Recent work in the construction of 3D scene graphs has enabled mobile robots to build large-scale metric-semantic hierarchical representations of the world. These detailed models contain information that is useful for planning, however an open question is how to derive a planning domain from a 3D scene graph that enables efficient computation of executable plans. In this work, we present a novel approach for defining and solving Task and Motion Planning problems in large-scale environments using hierarchical 3D scene graphs. We describe a method for building sparse problem instances which enables scaling planning to large scenes, and we propose a technique for incrementally adding objects to that domain during planning time that minimizes computation on irrelevant elements of the scene graph. We evaluate our approach in two real scene graphs built from perception, including one constructed from the KITTI dataset. Furthermore, we demonstrate our approach in the real world, building our representation, planning in it, and executing those plans on a real robotic mobile manipulator. A video supplement is available at \url{https://youtu.be/v8fkwLjBn58}.
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