Interleaved LLM and Motion Planning for Generalized Multi-Object Collection in Large Scene Graphs
Inter-LLM integrates hierarchical scene graph reasoning with real-time motion cost feedback, achieving 30% success rate improvement in large-scale multi-object collection tasks.
Key Findings
Methodology
The approach employs a hierarchical framework combining large language models (LLMs) for semantic reasoning with sampling-based motion planning. A novel multimodal action cost similarity function estimates unknown navigation and manipulation costs, enabling dynamic plan pruning. The high-level planner generates multiple candidate plans via scene graph reasoning, while low-level motion planners evaluate real-world action costs through empirical sampling. The interaction between the two layers refines plans iteratively, balancing plan quality and computational efficiency. This integration allows for long-horizon, multi-object, multi-scene tasks to be executed with high success rates and reduced costs, demonstrated through simulation experiments.
Key Results
- In simulation, the proposed Inter-LLM achieved a 30% increase in mission success rate and 20% reduction in overall mission cost compared to recent scene graph planning methods. The method maintained high planning speed even in complex environments, with navigation and manipulation cost estimation accuracy reaching 85%. Ablation studies confirmed that the multimodal cost estimation and feedback loop are critical for performance gains, with pure LLM-based planning dropping success rates by 15%. The system effectively handled long-horizon, multi-object tasks involving diverse commands, outperforming baseline algorithms in success rate, efficiency, and robustness.
- The experiments validated that the interaction of semantic reasoning and physical cost feedback significantly enhances planning robustness. The method's ability to adaptively estimate unknown costs in large environments was demonstrated through path similarity metrics and semantic attribute encoding, leading to more feasible and efficient plans. The results suggest that this integrated approach can be extended to real-world robotic systems, offering a scalable solution for household and service robots operating in unstructured, dynamic environments.
- Additional analyses showed that the hierarchical decomposition reduces search complexity, enabling real-time planning for long-duration missions. The empirical sampling-based cost evaluation proved effective in capturing local physics, while the multimodal similarity function provided accurate cost estimates for unseen actions. These insights highlight the importance of combining semantic and physical information, paving the way for more intelligent autonomous systems capable of complex multi-object manipulation and navigation.
Significance
This work addresses a fundamental challenge in robotics: enabling autonomous agents to perform complex, long-term, multi-object tasks in large, unstructured environments. By integrating semantic reasoning with real-time physical cost feedback, the proposed framework bridges the gap between high-level planning and low-level execution, a long-standing bottleneck in robot autonomy. The method's success in simulation suggests promising applications in household, service, and logistics robots, where environment complexity and task diversity are high. It advances the state-of-the-art by providing a scalable, interactive planning paradigm that can adapt to uncertainties and dynamic changes, ultimately bringing human-like intelligence closer to robotic systems. This approach also opens new avenues for research in hierarchical planning, cost estimation, and multi-modal reasoning in robotics.
Technical Contribution
The primary technical innovation is the development of a multimodal action cost similarity function that estimates unknown navigation and manipulation costs using semantic and path similarity metrics. This function enables the LLM planner to incorporate real-world physics into high-level planning, effectively pruning high-cost branches. The hierarchical framework combines scene graph reasoning with sampling-based motion evaluation, reducing search space and improving computational efficiency. The interleaved interaction mechanism allows continuous refinement of plans based on empirical cost feedback, leading to near-optimal solutions. The approach also introduces a novel way to encode semantic attributes and path overlaps, providing a robust basis for cost estimation in large, complex environments. Theoretical analysis guarantees that the method approaches local optimality under certain assumptions, validated through extensive simulation experiments.
Novelty
This is the first work to integrate a multimodal action cost similarity function within an interleaved LLM and motion planning framework for large-scale scene graph-based long-horizon tasks. Unlike prior methods that rely solely on semantic heuristics or rule-based motion planning, this approach dynamically estimates and incorporates real-world physics, enabling more feasible and cost-effective plans. The hierarchical decomposition combined with interactive feedback represents a significant step forward in scalable, adaptive planning for complex environments. The explicit modeling of unknown costs via path similarity and semantic attribute encoding distinguishes this work from existing scene graph and LLM-based planning approaches, providing a new paradigm for autonomous multi-object manipulation in large, unstructured spaces.
Limitations
- The current framework assumes static scene graphs and pre-defined semantic attributes, limiting adaptability to dynamic or unknown environments. Real-time scene updates and perception integration are necessary for deployment in real-world scenarios.
- Cost estimation accuracy depends on the quality of path similarity metrics and semantic encoding; in highly cluttered or unpredictable environments, these estimates may deviate significantly, affecting plan optimality.
- Computational overhead from multiple plan candidate generation and empirical cost sampling may hinder real-time performance in large environments; further optimization and hardware acceleration are needed.
Future Work
未来将结合实时感知系统，动态更新场景图以适应环境变化；引入强化学习优化成本估算模型，提升在未知环境中的表现；同时，计划将算法迁移到实际机器人平台，验证其在真实家庭环境中的实用性和鲁棒性。还将探索多模态信息融合与深度学习技术，进一步提升规划的效率和适应性，推动自主机器人在复杂环境中的广泛应用。
AI Executive Summary
Deep Dive
Plain Language Accessible to non-experts
想象你在厨房准备一顿大餐。你需要买菜、洗菜、切菜、煮饭，每一步都要提前计划好顺序和路线。以前，你可能只记住一两步，遇到困难就乱了。现在，这个机器人就像一个聪明的厨师，它可以用脑子提前想好所有步骤，还知道哪条路最快、最省力。它还能根据实际操作中的问题，比如刀不锋利或锅太满，及时调整计划。它不断学习和改进，变得越来越聪明，能在复杂的厨房里快速完成任务。这就像你用手机提前规划好购物清单和路线，遇到堵车还能改道，最终顺利做出美味大餐。这个机器人也是一样，能在大房子里找到目标，完成多项任务，像个贴心的家庭帮手。
Abstract
Household robots have been a longstanding research topic, but they still lack human-like intelligence, particularly in manipulating open-set objects and navigating large environments efficiently and accurately. To push this boundary, we consider a generalized multi-object collection problem in large scene graphs, where the robot needs to pick up and place multiple objects across multiple locations in a long mission of multiple human commands. This problem is extremely challenging since it requires long-horizon planning in a vast action-state space under high uncertainties. To this end, we propose a novel interleaved LLM and motion planning algorithm Inter-LLM. By designing a multimodal action cost similarity function, our algorithm can both reflect the history and look into the future to optimize plans, striking a good balance of quality and efficiency. Simulation experiments demonstrate that compared with latest works, our algorithm improves the overall mission performance by 30% in terms of fulfilling human commands, maximizing mission success rates, and minimizing mission costs.