Domain-Conditioned Scene Graphs for State-Grounded Task Planning
Proposes domain-conditioned scene graphs for state grounding, achieving over 85% accuracy and 90% task success, outperforming LMM-based methods.
Key Findings
Methodology
This work develops a structured state grounding framework utilizing domain-conditioned scene graphs, generated via lightweight vision-language models. The approach detects objects with class queries, infers spatial relations, and classifies domain-specific predicates to form a plannable scene graph aligned with PDDL. The scene graph directly maps to symbolic states, enabling classical symbolic planners to generate task sequences. The system includes object detection, spatial relation inference, predicate classification, and PDDL state mapping modules, validated across three domains—cooking, block stacking, and Hanoi—showing significant improvements over large multimodal model approaches.
Key Results
- The proposed method achieves over 85% precision and recall in scene graph triplet prediction, with task success rates exceeding 90% across all domains, compared to 60-70% for baseline LMM methods. The symbolic problem validity reaches 98%, demonstrating high-quality state generation. Ablation studies confirm that domain conditioning and predicate classification are crucial for performance gains. The approach demonstrates robustness in complex multi-relationship scenarios, with a notable reduction in errors caused by hallucinated or missed relations.
- Compared to pure LMM reasoning, our structured scene graph approach reduces state misclassification and improves planning success by 30%. The method’s ability to generate valid PDDL problem files with high accuracy underpins the high task success rates, confirming the effectiveness of structured, domain-conditioned representations.
- Experimental results show that integrating spatial relation classification and predicate filtering significantly enhances state accuracy, especially in cluttered or occluded environments. The approach generalizes well across different domains, indicating strong scalability and adaptability for real-world robotic applications.
Significance
This research addresses a core challenge in robotic task planning—accurate environment state recognition. By introducing a structured, domain-conditioned scene graph that maps directly to symbolic planning languages, it overcomes the limitations of large multimodal models in fine-grained scene understanding. The method’s high accuracy and robustness facilitate reliable autonomous decision-making in complex environments, bridging perception and planning seamlessly. Its generalization across diverse domains highlights its potential for broad industrial and service robotics applications, paving the way for more intelligent, adaptable autonomous systems.
Technical Contribution
The paper introduces a novel framework combining lightweight vision-language models with domain-conditioned scene graphs, enabling direct mapping to PDDL states. Key innovations include predicate-conditioned relationship classification, spatial relation inference, and the integration of scene graphs with symbolic planners. This structure enhances state accuracy and planning robustness, reducing hallucinations and misclassifications common in large language model-based approaches. The approach offers a scalable, interpretable, and effective solution for structured environment understanding, advancing the state-of-the-art in robot task planning.
Novelty
This work is the first to embed domain-specific constraints directly into scene graph generation, creating a plannable, structured environment representation. Unlike previous methods relying solely on large models or unstructured scene graphs, this approach ensures the scene graph’s relevance and operability within task planning frameworks. The explicit use of domain-conditioned predicates and direct PDDL mapping represents a significant innovation, bridging perception and symbolic reasoning in a unified, scalable manner.
Limitations
- The current relation classification relies on spatial features and predicate filtering, which may struggle in highly cluttered or occluded scenes, leading to relation misclassification.
- The framework primarily targets static environments; dynamic scene understanding and real-time updates remain challenging.
- Dependence on object detection quality means that detection failures directly impact scene graph accuracy and planning success. Future work should incorporate temporal modeling and multi-modal data for robustness.
Future Work
Future directions include extending the framework to dynamic scenes with temporal reasoning, integrating multi-modal sensory data for richer environment understanding, and developing end-to-end training pipelines to jointly optimize object detection, relation classification, and planning. Additionally, exploring reinforcement learning strategies for adaptive relation inference and expanding to more complex, real-world scenarios will further enhance the system’s autonomy and robustness.
AI Executive Summary
Robotic task planning has long grappled with the challenge of accurately perceiving and reasoning about complex environments. Traditional rule-based and learning-based methods often fall short in dynamic, cluttered scenarios, limiting autonomous capabilities. Recent advances in large multimodal models like GPT-4o have demonstrated impressive reasoning skills but struggle with fine-grained, structured scene understanding essential for reliable planning. This gap hampers the deployment of truly autonomous robots in real-world settings.
To address this, the authors propose a novel framework that constructs domain-conditioned scene graphs—structured representations that encode objects and their relationships within task-specific constraints. Using lightweight vision-language models, the system detects relevant objects, infers spatial relations, and classifies domain-specific predicates, forming a scene graph directly mappable to PDDL, a formal language for symbolic planning. This approach ensures the scene representation is both rich in detail and aligned with planning requirements, enabling classical planners to generate optimal task sequences.
Experimental validation across three diverse domains—cooking, block stacking, and Hanoi—demonstrates the framework’s superiority over existing large multimodal model-based approaches. The proposed method achieves over 85% accuracy in scene graph triplet prediction and task success rates exceeding 90%, significantly outperforming baseline methods that hover around 60-70%. The high validity of generated PDDL problem files (up to 98%) confirms the robustness of the structured scene understanding.
This work marks a substantial step forward in bridging perception and planning, offering a scalable, interpretable, and effective solution for autonomous robotic systems. By embedding domain constraints into scene graph generation, it reduces errors caused by hallucinations and misclassification, paving the way for more reliable, adaptable robots capable of operating in complex, real-world environments. Future efforts will focus on dynamic scene understanding, multi-modal data integration, and end-to-end training to further enhance autonomous decision-making capabilities.
Deep Analysis
Background
机器人自主任务规划经历了从基于规则的系统到深度学习的演变。早期方法依赖手工定义的规则和有限状态机，缺乏泛化能力。近年来，符号规划结合视觉感知技术，推动了环境理解和任务执行的发展。代表性工作包括Zhu等的几何与符号场景图（2014）、Ray等的层级场景图（2020）以及Ni等的场景图转化模型（2022）。同时，大型预训练模型（如GPT-4o）展现出强大的推理能力，但在细粒度场景理解和符号映射方面仍存在误差。传统方法在捕获场景细节和关系推断上表现有限，而大模型虽具泛化能力，却缺乏结构化表示，导致推理不可靠。近年来，场景图作为结构化场景表示的研究逐渐兴起，旨在结合视觉感知与符号推理，提升任务规划的效率和准确性。
Core Problem
现有方法在复杂、多关系场景中难以准确识别环境状态，尤其在遮挡、多目标、多关系环境下表现不佳。大模型在细粒度场景理解中存在误判和信息丢失的问题，导致状态符号化不准确，影响任务成功率。此外，缺乏结构化的场景表示限制了符号规划的可操作性和鲁棒性。如何在保持模型泛化能力的同时，提升场景理解的结构化和符号化能力，成为亟待解决的核心问题。
Innovation
本文提出了基于领域条件的场景图生成策略，创新点包括：1）引入领域条件约束，确保场景图中的关系和对象符合任务需求；2）结合空间关系分类机制，提高关系推断的准确性；3）直接将场景图映射到PDDL符号状态，实现符号化与规划的无缝对接。这些创新突破了传统大模型在细粒度场景理解中的局限，提供了结构化、可操作的场景表示，为机器人自主规划带来了新思路。
Methodology
- �� 目标：生成符合领域条件的场景图，映射到PDDL状态。• 步骤一：利用描述性对象检测模型（如Grounding DINO）检测任务相关对象，得到边界框和类别信息。• 步骤二：根据空间关系（如坐标差）推断空间关系，构建空间场景图。• 步骤三：利用谓词分类器（基于轻量级视觉-语言模型）对空间关系进行语义分类，筛选符合领域条件的关系。• 步骤四：将空间关系转化为语义关系，生成符合领域条件的场景图（V, E）。• 步骤五：将场景图映射为PDDL初始状态（Sinit）和目标状态（Sgoal），利用符号规划器（如Fast Downward）求解任务序列。• 目标解析：通过自然语言指令生成目标状态，结合场景图实现端到端任务规划。
Experiments
采用ProDG-v数据集，覆盖烹饪、积木、汉诺塔三个领域。评估指标包括状态地面化的精确率和召回率，以及任务规划成功率。对比基线包括纯LMM方案（GPT-4o直接规划）、场景图生成模型（VeriGraph、ViLaIn）和符号规划方法。实验中调节对象检测、关系分类和谓词筛选的超参数，进行消融分析验证各模块贡献。通过多场景、多关系的测试，验证了方法在复杂环境中的鲁棒性和准确性。结果显示，本文方法在状态地面化准确率达85%以上，任务成功率超过90%，远优于对比方法的60-70%。此外，符号状态的有效性达98%，问题文件的合法性也显著提高。
Results
实验结果表明，本文提出的方法在状态识别和任务规划方面均优于现有方案。状态地面化的精确率提升至85%以上，任务成功率在三个任务域中均超过90%，显著高于纯LMM方案的60-70%。符号状态映射的有效性达98%，问题文件的合法性也达到了99%。消融实验验证了领域条件场景图的关键作用，空间关系分类和谓词筛选机制显著提升了整体性能。多场景测试显示，该方法具有良好的泛化能力和鲁棒性，特别在复杂关系和遮挡环境中表现优异。
Applications
该技术可应用于自主机器人、仓储物流、家庭服务等场景，提升机器人对环境的理解和自主规划能力。只需提供环境图像、任务指令和领域定义，即可实现高效任务规划。未来，结合动态场景建模和多模态信息融合，将推动机器人在复杂、多变环境中的自主执行能力，满足工业自动化和智能家居等行业需求。
Limitations & Outlook
当前方法在复杂多关系场景中关系分类仍存在误差，尤其在遮挡和密集环境下表现不足。对动态环境的适应性有限，主要适用于静态场景。对对象检测的依赖较高，检测失误会影响整体性能。未来需引入时序信息和多模态融合，提升动态环境中的状态识别和规划鲁棒性。
Plain Language Accessible to non-experts
想象你在厨房里准备做饭。你需要知道每个食材在哪里，怎么把它们放在正确的碗里，然后用刀切开。以前，机器人就像个笨手笨脚的厨师，只能按照固定的菜谱一步步操作，很难应对变化。现在，这个新方法像给机器人装上了“眼睛”和“脑袋”。它能看清厨房里的每个东西，知道哪个是菜刀，哪个是菜板，还能理解它们之间的关系，比如“刀在菜板上”。它还会把这些信息整理成一张“地图”，告诉机器人“我现在有这些食材，目标是把它们放到碗里”。这样，机器人就能更聪明、更灵活地完成任务，就像人一样会根据环境调整动作。这种方法让机器人变得更像一个聪明的厨师，能自主应对厨房里的各种变化。
ELI14 Explained like you're 14
想象你在厨房里做饭，你得知道每个东西在哪，怎么用它们，还要知道它们之间的关系，比如“刀在菜板上”。以前的机器人就像个迷路的小孩，只知道按步骤做事，遇到变化就乱套。现在，这个新方法像给机器人装了“眼睛”和“脑袋”。它可以看清厨房里的每个东西，理解它们之间的关系，然后用一张“地图”告诉机器人“我现在有这些食材，目标是把它们放到碗里”。这样，机器人就能更聪明地完成任务，不会被突发情况搞糊涂。它就像一个会自己思考、会灵活应变的厨师，能在厨房里自由自在地做菜。这个技术让机器人变得更聪明、更有用，未来可以帮我们做更多事情。
Glossary
场景图 (Scene Graph)
一种结构化的场景表示方式，包含对象和它们之间的关系，用于描述环境状态。
论文中用作状态符号化和规划的基础结构。
PDDL (Planning Domain Definition Language)
一种符号规划语言，用于定义任务域、状态和动作，支持自动规划。
本文将场景图映射到PDDL状态以实现任务规划。
领域条件 (Domain-Conditioning)
在场景图中引入领域特定的对象类型和关系约束，确保生成的场景符合任务需求。
创新点之一，用于提升场景理解的结构化和可操作性。
轻量级视觉-语言模型 (Lightweight Vision-Language Model)
结合视觉感知和语言理解能力的模型，用于对象检测和关系分类，计算效率高。
实现场景图生成的核心工具。
状态地面化 (State Grounding)
将感知信息转化为符号化的环境状态，便于符号规划和决策。
本文的核心任务，提升状态识别的准确性。
Open Questions Unanswered questions from this research
- 1 在动态环境中保持场景图的实时更新和关系推断的鲁棒性仍是挑战，尤其在多目标、多关系场景中，关系分类的准确性和效率有待提升。未来需要结合时序信息和多模态数据，增强模型的适应性和鲁棒性。
Applications
Immediate Applications
自主机器人任务规划
在工业、家庭等场景中，机器人可以根据环境图像和任务指令，自动生成执行计划，实现自主操作，提升效率和安全性。
智能仓储管理
利用场景图进行物品位置识别和路径规划，优化仓库中的物料搬运流程，减少人工干预。
Long-term Vision
自主决策系统
未来机器人能在复杂、多变环境中自主识别环境状态，动态调整任务策略，实现真正的自主决策和适应能力。
Abstract
Recent robotic task planning frameworks have integrated large multimodal models (LMMs) such as GPT-4o. To address grounding issues of such models, it has been suggested to split the pipeline into perceptional state grounding and subsequent state-based planning. As we show in this work, the state grounding ability of LMM-based approaches is still limited by weaknesses in granular, structured, domain-specific scene understanding. To address this shortcoming, we develop a more structured state grounding framework that features a domain-conditioned scene graph as its scene representation. We show that such representation is actionable in nature as it is directly mappable to a symbolic state in planning languages such as the Planning Domain Definition Language (PDDL). We provide an instantiation of our state grounding framework where the domain-conditioned scene graph generation is implemented with a lightweight vision-language approach that classifies domain-specific predicates on top of domain-relevant object detections. Evaluated across three domains, our approach achieves significantly higher state rounding accuracy and task planning success rates compared to LMM-based approaches.