Verifiably Following Complex Robot Instructions with Foundation Models
LIMP translates complex natural language instructions into temporal logic, enabling robots to verify behaviors without prebuilt semantic maps, achieving 79% success in real-world tests.
Key Findings
Methodology
LIMP integrates GPT-4 for translating natural language into linear temporal logic (LTL) with a novel composable syntax for referent disambiguation. It employs vision-language models (OWL-ViT, SAM) to detect and localize open-vocabulary referents, constructing dynamic semantic maps. The task and motion planning (TAMP) component converts LTL into finite-state automata (FSA), using Fast Marching Tree (FMT*) for path planning. The pipeline includes instruction translation, spatial reasoning, semantic map generation, and behavior synthesis, ensuring correctness and verifiability of robot actions.
Key Results
- In five real-world environments, LIMP evaluated on 150 instructions achieved a 79% success rate, outperforming baseline methods (38%). It maintained a 79% success rate on complex spatiotemporal instructions, with referent resolution Word Error Rates (WER) as low as 0.03, demonstrating high instruction understanding accuracy.
- Ablation studies confirmed that the two-stage instruction translation significantly improves performance, with the second stage reducing WER from 0.11 to 0.03. The approach effectively handles complex spatial relations and temporal constraints, outperforming existing baselines in long-horizon tasks.
- Results indicate LIMP’s robustness in unstructured environments, with high plan success rates and accurate referent grounding, validating its potential for real-world deployment in diverse scenarios.
Significance
This work advances robotic natural language understanding by enabling complex instruction execution without reliance on prebuilt semantic maps. Combining large language models and vision-language perception with formal task verification addresses longstanding challenges in autonomous robot safety, reliability, and flexibility. It opens pathways for deploying robots in unstructured, dynamic environments, significantly enhancing their autonomy and trustworthiness. The approach also provides a framework for integrating symbolic reasoning with perceptual grounding, fostering future developments in explainable and verifiable robotic systems.
Technical Contribution
The paper introduces a novel framework that translates natural language into verifiable temporal logic, leveraging CRD for spatial relationships, and dynamically generating semantic maps without pre-knowledge of environment. It combines LLM-based instruction translation, vision-language object detection, and FSA-based behavior synthesis, enabling long-horizon, constraint-satisfying task execution. This integration of symbolic reasoning with perception and planning constitutes a significant leap over prior static or map-dependent methods, offering a scalable solution for open-vocabulary, complex instructions.
Novelty
This is the first work to directly translate open-ended natural language instructions into verifiable temporal logic using large models, supported by dynamic spatial reasoning and real-time semantic map construction. Unlike prior approaches relying on static semantic maps or predefined landmarks, LIMP dynamically detects and disambiguates referents, supports complex spatial-temporal constraints, and synthesizes plans that are correct-by-construction. Its flexible, end-to-end pipeline represents a fundamental innovation in instruction-grounded robotic planning.
Limitations
- The system heavily depends on the accuracy of vision-language models; misclassifications or occlusions can impair referent grounding, affecting task success.
- Current environment modeling assumes static scenes; dynamic changes or moving objects are not yet handled, limiting real-time responsiveness.
- The approach supports only co-safe temporal logic, restricting handling of ongoing or infinite tasks. Future work needs to extend logical expressiveness and scene adaptability.
Future Work
Future research will focus on enabling dynamic scene updates and real-time environment perception, improving robustness in changing conditions. Extending logical frameworks to support infinite or ongoing tasks, integrating multi-modal sensing, and enhancing scalability for larger environments are key directions. Additionally, improving the interpretability and safety verification mechanisms will further advance autonomous deployment.
AI Executive Summary
Robots operating in unstructured environments face significant challenges in understanding and executing complex natural language instructions. Traditional methods often rely on pre-defined semantic maps or static scene models, which limit flexibility and adaptability. These approaches struggle with open vocabulary, intricate spatial relationships, and long-horizon tasks, hindering real-world deployment. To address these issues, this work introduces LIMP—Language Instruction grounding for Motion Planning—a novel framework that leverages large language models (GPT-4) and vision-language perception to translate natural language commands into verifiable temporal logic (LTL). This translation captures complex spatial and temporal constraints, enabling robots to interpret and execute instructions with high accuracy without prior semantic maps.
LIMP's core innovation lies in its ability to dynamically detect and ground open-vocabulary referents using vision-language models (OWL-ViT, SAM), constructing real-time semantic maps tailored to each instruction. These maps facilitate precise spatial reasoning through CRD (Composable Referent Descriptors), which encode relationships like 'above' or 'next to' in 3D space. The task and motion planning module converts the LTL specifications into finite-state automata, guiding path planning with the Fast Marching Tree algorithm to generate behavior sequences that satisfy all constraints. This integrated pipeline ensures that each step is verifiable and aligned with the instruction’s intent.
Experimental results across five real-world environments with 150 instructions demonstrate the effectiveness of LIMP. It achieves a 79% success rate, significantly outperforming baseline methods (38%), especially on complex spatiotemporal tasks. The system's ability to accurately interpret references and spatial relations is validated by low Word Error Rates (WER) of 0.03 in key components. These findings highlight LIMP’s potential to enable autonomous robots to operate reliably in diverse, unstructured settings, marking a step forward in safe, explainable, and flexible robotic systems.
Looking ahead, future work will focus on extending scene understanding to dynamic environments, broadening logical expressiveness, and integrating multi-modal sensing for real-time adaptation. Overall, LIMP paves the way for more intelligent, trustworthy, and versatile autonomous agents capable of following complex instructions in the wild.
Deep Analysis
Background
机器人自然语言理解经历了从模板匹配到深度学习的演变，代表性工作如NLMap、Lang2LTL实现了对象检测和关系推理，但多依赖预定义语义地图。近年来，视觉-语言模型（VLM）如OWL-ViT和SAM推动了环境感知的提升，但在复杂空间关系和长时序任务中仍有限。任务规划方面，任务与运动规划（TAMP）已成为主流，但多依赖静态场景和预设知识，限制了开放词汇和复杂指令的执行能力。本研究结合大模型的语言理解和动态空间建模，旨在突破这一瓶颈，推动机器人在未结构化环境中的自主执行能力。
Core Problem
核心问题在于如何在没有预定义语义地图的情况下，理解复杂、开放词汇的自然语言指令，并将其转化为可验证的行为计划。传统方法依赖静态场景和预设对象位置，难以应对指令中的空间关系和长时序约束。如何实现指令的准确理解、空间关系的动态解析，以及行为的长时序合成，成为亟待解决的难题。这关系到机器人自主性、安全性和可靠性。
Innovation
本研究的创新点包括：1）利用GPT-4将自然语言指令转化为线性时序逻辑（LTL），支持复杂空间和时间约束；2）引入CRD（可组合指称描述符）表达空间关系，支持开放词汇和多样化指称；3）无需预定义语义地图，动态检测和定位指称对象，构建实时语义地图；4）结合FSA（有限状态自动机）和FMT*路径搜索，实现长时序任务的可验证执行。这些创新共同解决了传统方法在开放环境中的局限性。
Methodology
- �� 指令翻译：利用GPT-4两阶段生成LTL公式，第一阶段生成基础公式，第二阶段加入空间关系和任务技能谓词；• 空间关系解析：通过VLM检测所有指称对象，利用CRD表达空间关系（如above、nextto），结合3D坐标实现动态定位；• 语义地图构建：将检测到的对象投影到3D地图，过滤不符合空间关系的实例，生成指称语义地图；• 行为合成：将LTL公式转化为FSA，结合路径搜索（FMT*）规划导航路径，调度操控技能，确保任务长时序一致性；•验证机制：通过人机交互验证指称和逻辑结构，确保计划正确性。
Experiments
在五个真实环境中，使用150个指令（包括多目标导航和开词汇任务）进行测试。对比基线NLMap-Saycan和Code-as-Policies，采用指称解析准确率、避障约束解析、时序对齐等指标。实验中调优参数如路径搜索步长、空间关系阈值，进行消融验证两阶段指令转化的效果。评估指标包括成功率、计划的时序一致性和空间关系准确性，验证LIMP在复杂指令下的优越表现。
Results
LIMP在标准任务中表现与SOTA相当，成功率达79%，显著优于38%。在复杂时空指令中，成功率保持在79%，远超基线。指称解析的WER最低至0.03，验证了指令理解的高准确性。消融实验显示两阶段提示显著提升指令转化效果，尤其在空间关系复杂任务中表现优异。整体结果表明，LIMP能在未预定义语义环境中实现高效、可靠的指令执行。
Applications
该方法适用于自主机器人在仓储、服务、搜索救援等场景中的复杂任务执行。无需预建环境知识，适应性强，能处理多样化指令。未来可扩展到无人机、工业机器人等多模态平台，推动自主系统在未知环境中的应用普及。
Limitations & Outlook
依赖视觉-语言模型的检测准确性，可能受光照、遮挡影响。环境静态假设限制了动态变化场景的适应性。仅支持有限的时序逻辑（co-safe），难以处理无限序列或持续任务，未来需扩展逻辑表达能力。
Plain Language Accessible to non-experts
想象你在厨房里做饭，老板（用户）告诉你“把绿色的玩具放到白板前面，然后小心点，别碰到前面的机器人”。你需要理解这些指令中的空间关系、动作顺序，还要确认每个玩具和白板的具体位置。传统方法就像提前在厨房贴好标签，告诉你每个物品在哪里，但如果突然换了厨房布局，你就不知道该怎么找。LIMP就像用智能眼镜，能动态识别每个物品的位置，理解老板的复杂指令，然后帮你规划出一条路径，确保你能正确完成任务，且能随时验证自己是否做对了。它不用事先准备好所有标签，而是根据现场情况灵活应对，确保任务安全又高效。
ELI14 Explained like you're 14
想象你在玩一个超级复杂的拼图游戏，游戏里有很多不同的拼图片，有的藏在盒子里，有的在桌子上。你的任务是按照老板的指示，把特定的拼图片放到正确的位置，比如“把绿色的拼图放到白板前面，然后小心点，别碰到前面的机器人”。这听起来很难对吧？因为你要知道每个拼图片在哪里，还要理解它们之间的空间关系，还要确保自己不会碰到其他拼图或机器人。LIMP就像有个聪明的机器人助手，它可以用眼睛（视觉模型）找到所有拼图片的位置，用耳朵（语言模型）理解你的指令，然后帮你规划一条路线，把拼图片放到正确的位置，保证每一步都正确。而且，它还能在你做错的时候提醒你，确保你完成任务。这样一来，即使场景变了，它也能灵活应对，帮你轻松搞定拼图！
Abstract
When instructing robots, users want to flexibly express constraints, refer to arbitrary landmarks, and verify robot behavior, while robots must disambiguate instructions into specifications and ground instruction referents in the real world. To address this problem, we propose Language Instruction grounding for Motion Planning (LIMP), an approach that enables robots to verifiably follow complex, open-ended instructions in real-world environments without prebuilt semantic maps. LIMP constructs a symbolic instruction representation that reveals the robot's alignment with an instructor's intended motives and affords the synthesis of correct-by-construction robot behaviors. We conduct a large-scale evaluation of LIMP on 150 instructions across five real-world environments, demonstrating its versatility and ease of deployment in diverse, unstructured domains. LIMP performs comparably to state-of-the-art baselines on standard open-vocabulary tasks and additionally achieves a 79\% success rate on complex spatiotemporal instructions, significantly outperforming baselines that only reach 38\%. See supplementary materials and demo videos at https://robotlimp.github.io