Large Language Model-Based Robot Planning

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25 papers in the last four weeks, up 525% on the four weeks before. 0.2% of all new papers.

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May 16, 2026cs.RO

NORM-Nav: Zero-Shot Mobile Robot Navigation with Natural Language Behavioral Constraints

Mobile robots operating in human-centered environments must generate not only collision-free paths but also trajectories that follow local behavioral conventions. Conventional costmap-based navigation emphasizes geometric feasibility and often overlooks such requirements, which can result in socially inappropriate behaviors. This paper presents NORM-Nav, a zero-shot framework that integrates natural language behavioral constraints into costmap-based planning. An LLM parses each instruction into structured constraints and grounds them using real-time vision--LiDAR perception. These constraints are encoded as multi-layer costmaps that represent geometric, semantic, directional, and velocity cues and are directly compatible with standard grid-based planners. Simulation and real-world experiments indicate that NORM-Nav improves task success rates and produces trajectories closer to human references than representative baselines. The project website is available at https://ei-nav.github.io/NORM-Nav.

Dongjie Huo, Junhui Wang, Chao Gao +3

College of Information Science and Technology, Beijing University of Chemical Technology · Institute for AI Industry Research (AIR), Tsinghua University · Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems, Macau University of Science and Technology +1

Constrained Motion Planning Trajectory Generation Large Language Model-Based Robot Planning Social Robot Navigation Language-Conditioned Robot Control Robot Motion Planning

May 15, 2026cs.RO

Propagating Unsafe Actions in LLM Controlled Multi-Robot Collaboration via Single Robot Compromise

Large language models (LLMs) are increasingly used as general planners in embodied intelligence, enabling high level coordination and low level task planning for both single robot and multi-robot collaboration. This increasing reliance on embodied LLM planners also raises critical security concerns, since misaligned or manipulated instructions can be translated into physical actions. Prior work has studied such threats in single robot settings, while security risks in LLM controlled multi-robot collaboration, especially those propagated through inter robot communication, remain largely unexplored. To bridge this gap, we propose a novel attack paradigm for multi-robot system in which the adversary interacts with only a single entry robot. The compromised robot then propagates malicious intent through peer communication, leading to coordinated unsafe actions across the system. Our evaluation, covering high risk dimensions of dereliction of duty, privacy compromise, and public safety hazards, reveals a persistent safety alignment gap in multi-robot planners. We quantify this process with three metrics, obedience, infectiousness, and stealthiness. Experiments demonstrate both persistent attacker control and rapid propagation: obedience reaches 1.00 in the strongest cases, and infectiousness rises to 0.90. Notably, the attack is highly efficient, requiring as few as 3.0 rounds to compromise all the robots while maintaining a stealthiness score of 0.81. Such risks are amplified when robots must resolve trade offs in critical situations, such as emergencies or conflicts of rights, because the coordination mechanism can unintentionally allow adversarial instructions to override safety requirements. The code is available at https://github.com/TheFatInsect/InfectBot.

Zhen Huang, Zhihuang Liu, Mengxuan Luo +2

College of Computer Science and Technology, National University of Defense Technology

Large Language Model-Based Robot Planning Adversarial Attacks on LLMs LLM Safety Alignment Multi-Robot Coordination Robot Safety Multi-Agent System Security

May 15, 2026cs.RO

Hybrid LLM-based Intelligent Framework for Robot Task Scheduling

This study introduces intelligent frameworks that use Large Language Models (LLMs) to improve task scheduling for construction robots. The LLM is fed with key data about the desired task, such as agent action abilities, and the desired end goal to be achieved. A well-balanced allocation strategy is developed, optimizing both time efficiency and resource utilization. Our system utilizes a Natural Language Processing interface to streamline communication with construction professionals and adapt in real-time to unexpected site conditions. We concurrently use two LLM agents, specifically generator (GPT-4) and supervisor (Gemma 3/Llama 4/Mistral 7b) LLM agents to provide a more precise task schedule. We evaluate the proposed methodology using a straightforward scenario and provide metric scores to prove the efficacy of the frameworks. Our results highlight that the implementation of LLMs is crucial in construction operational tasks including robots.

Swayamjit Saha, Subhabrata Das, Haonan Duan +1

Department of Computer Science and Engineering, Mississippi State University, Starkville, MS, USA · 2Graduate School of Arts and Sciences, Columbia University, New York, NY, USA · 3Consumer and Community Banking, JPMorgan Chase, New York, NY, USA +2

Robotic Construction Large Language Model-Based Robot Planning LLM Agent Orchestration Robotics

May 13, 2026cs.RO

LMPath: Language-Mediated Priors and Path Generation for Aerial Exploration

Traditional autonomous UAV search missions rely on geometric coverage patterns that ignore the semantic context of the target, leading to significant time waste in large-scale environments. In this paper we present LMPath, a pipeline for generating language-mediated exploration priors for Unmanned Aerial Vehicle (UAV) search missions that leverages semantics. Given a basic geofence and an object of interest prompt, LMPath uses generative language models to determine what regions of the environment should contain that object and a foundation vision model ran over satellite imagery to segment sub-regions that form the exploration prior. This prior can then be used to generate UAV paths with various objectives, such as minimizing the expected time to locate the object of interest, maximizing the probability that the object is found given a limited travel distance, or narrowing down the search space to sub-regions that are most likely to contain the object. To demonstrate it's capabilities, we used LMPath to generate various UAV paths and ran them using a real UAV over large-scale environments. We also ran simulations to demonstrate how paths generated using LMPath outperform traditional path planning approaches for search missions.

Jonathan A. Diller, Fernando Cladera, Camillo J. Taylor +1

GRASP Laboratory, University of Pennsylvania

Trajectory Generation Large Language Model-Based Robot Planning UAV Navigation Robot Motion Planning

May 11, 2026cs.RO

Plan in Sandbox, Navigate in Open Worlds: Learning Physics-Grounded Abstracted Experience for Embodied Navigation

Vision-Language Models (VLMs) have demonstrated exceptional general reasoning capabilities. However, their performance in embodied navigation remains hindered by a scarcity of aligned open-world vision and robot control data. Despite simulators providing a cost-effective alternative for data collection, the inherent reliance on photorealistic simulations often limits the transferability of learned policies. To this end, we propose \textit{\textbf{S}andbox-\textbf{A}bstracted \textbf{G}rounded \textbf{E}xperience} (\textbf{\textit{SAGE}}), a framework that enables agents to learn within a physics-grounded semantic abstraction rather than a photorealistic simulation, mimicking the human capacity for mental simulation where plans are rehearsed in simplified physics abstractions before execution. \textit{SAGE} system operates via three synergistic phases: (1) \textit{Genesis}: constructing diverse, physics-constrained semantic environments to bootstrap experience; (2) \textit{Evolution}: distilling experiences through Reinforcement Learning (RL), utilizing a novel asymmetric adaptive clipping mechanism to stabilize updates; (3) \textit{Navigation}: bridging the abstract policy to open-world control. We demonstrate that \textit{SAGE} significantly improves planner-assisted embodied navigation, achieving a 53.21% LLM-Match Success Rate on A-EQA (+9.7% over baseline), while showing encouraging transfer to physical indoor robot deployment.

Zhixuan Shen, Jiawei Du, Ziyu Guo +5

School of Computing and Artificial Intelligence, Southwest Jiaotong University, China · Centre for Frontier AI Research, A*STAR, Singapore · School of Computer Science, University of Leeds, UK

RL for Robotics Embodied Navigation Large Language Model-Based Robot Planning Robotics Simulation Sim-to-Real Transfer

May 10, 2026cs.RO

LASSA Architecture-Based Autonomous Fault-Tolerant Control of Unmanned Underwater Vehicles

Unmanned underwater vehicles (UUVs) operate persistently in communication-constrained environments, thus requiring high-level autonomous fault-tolerant control under faulty operating conditions. Existing approaches rely heavily on predefined hard-coded rules and struggle to achieve effective fault-tolerant control against unforeseen faults. Although large language models (LLMs) possess powerful cognitive and reasoning capabilities, their inherent hallucinations remain a major obstacle to their application in UUV control systems. This paper proposes an intelligent control method based on the LASSA (LLM-based Agent with Solver, Sensor and Actuator) architecture. Within this architecture, an LLM identifies unknown faults and accomplishes task replanning via autonomous reasoning without hard-coded rules; the intelligent agent undertakes perception, scheduling and decision evaluation; the solver verifies physical boundary feasibility constraints prior to command transmission to the actuators. This architecture suppresses physically infeasible LLM hallucinations and ensures interpretable, verifiable decision-making. Moreover, it enables fast-slow dual closed-loop collaborative control, where the slow loop undertakes high-level dynamic decision-making and the fast loop guarantees high-frequency real-time control, simultaneously balancing decision intelligence and control timeliness. Lake experiments under normal and lower-rudder-fault conditions show that the framework detects trajectory tracking abnormalities, replans the route by adjusting the turning radius from 4m to 12m and reducing speed from 2kn to 1kn, passes all three solver constraints on the first invocation, and guides the UUV to complete the full mission; under normal conditions no false fault alarms are raised throughout the run.

Hong Chen, Zixiang Tang, Yuanbao Chen +1

Wuhan Second Ship Design and Research Institute, Wuhan, 430060, China · School of Aeronautic Science and Engineering, Beihang University, Beijing, 100191, China

Language Model-Based Control Large Language Model-Based Robot Planning Underwater Robotics

May 10, 2026cs.AI

NEXUS: Continual Learning of Symbolic Constraints for Safe and Robust Embodied Planning

While Large Language Models (LLMs) have catalyzed progress in embodied intelligence, a fundamental gap between their inherent probabilistic uncertainty and the strict determinism and verifiable safety required in the physical world. To mitigate this gap, this paper introduces NEXUS, a modular framework designed for continual learning in embodied agents. Different from prior works that treat symbolic artifacts merely as static interfaces, NEXUS leverages them for symbolic grounding and knowledge evolution. The framework explicitly decouples physical feasibility from safety specifications: capability of agents is improved through closed-loop execution feedback, while probabilistic risk assessments are grounded into deterministic hard constraints to establish a rigorous pre-action defense. Experiments on SafeAgentBench demonstrate that NEXUS achieves superior task success rates while effectively refusing unsafe instructions, exhibiting robust defense against adversarial attacks, and progressively improving planning efficiency through knowledge accumulation.

Tiehan Cui, Peipei Liu, Yanxu Mao +3

School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, China · School of Software, Henan University, China · Institute of Information Engineering, Chinese Academy of Sciences, China +2

Continual Learning for LLM Agents Large Language Model-Based Robot Planning LLM Agent Safety

May 8, 2026cs.RO

Hierarchical Prompting with Dual LLM Modules for Robotic Task and Motion Planning

We present a hierarchical language-driven framework for robotic task and motion planning to improve natural, intuitive human-robot interaction in service and assistance scenarios. The proposed system employs two large language model (LLM) modules: a high-level planning agent and a low-level spatial reasoning sub-module. The primary agent processes natural language commands and generates action sequences using a ReAct-style prompt, interacting with tools for object perception and manipulation (e.g., pick, place, release). For precise spatial placement, such as interpreting "place the mug next to the plate", a separate sub-prompting module handles 3D reasoning based on object geometry and scene layout. The system integrates YOLOX-GDRNet for object detection and pose estimation, along with a motion execution stub. We evaluated the system in 24 test scenarios, ranging from simple spatial commands to high-level instructions and infeasible requests. The system achieved an overall task success rate of 86%.

Karolina Źróbek, Tessa Pulli, Paweł Gajewski +2

IBM, Krakow, Poland · TU Wien, Vienna, Austria · Jagiellonian University, Krakow, Poland +1

Large Language Model-Based Robot Planning LLM Prompting Human-Robot Interaction Robot Motion Planning

May 8, 2026cs.RO

Melding LLM and temporal logic for reliable human-swarm collaboration in complex scenarios

Robot swarms promise scalable assistance in complex and hazardous environments. Task planning lies at the core of human-swarm collaboration, translating the operator's intent into coordinated swarm actions and helping determine when validation or intervention is required during execution. In long-horizon missions under dynamic scenarios, however, reliable task planning becomes difficult to maintain: emerging events and changing conditions demand continual adaptation, and sustained operator oversight imposes substantial cognitive burden. Existing LLM-based planning tools can support plan generation, yet they remain susceptible to invalid task orderings and infeasible robot actions, resulting in frequent manual adjustment. Here we introduce a neuro-symbolic framework for long-horizon human-swarm collaboration that tightly melds verifiable task planning with context-grounded LLM reasoning. We formalize mission goals and operational rules as temporal logic formulas and admissible task orderings as task automata. Conditioned on these formal constraints and live perceptual context, LLMs generate executable subtask sequences that satisfy mission rules and remain grounded in the current scene. An uncertainty-aware scheduler then assigns subtasks across the heterogeneous swarm to maximize parallelisms while remaining resilient to disruptions. An event-triggered interaction protocol further limits operator involvement to sparse, high-level confirmation and guidance. Deployment on a heterogeneous robotic fleet yields similar results while remaining robust to hardware-specific actuation and communication uncertainties. Together, these results support a formal and scalable paradigm for reliable and low-overhead human-swarm collaboration in dynamic environments

Junfeng Chen, Yuxiao Zhu, An Zhuo +6

School of Advanced Manufacturing and Robotics, Peking University, Beĳing & 100871, China. · 2Division of Natural and Applied Sciences, Duke Kunshan University, Suzhou & 215316, China. · School of Automaton, Southeast University, Nanjing & 210096, China. +1

Swarm Robotics Large Language Model-Based Robot Planning Robot Task Planning Multi-Robot Task Allocation Neuro-Symbolic AI

May 8, 2026cs.RO

CommandSwarm: Safety-Aware Natural Language-to-Behavior-Tree Generation for Robotic Swarms

Natural-language interfaces can make swarm robotics more accessible to non-expert operators, but they must translate ambiguous user intent into executable swarm behaviors without unsupported actions, malformed programs, or unsafe plans. This paper presents CommandSwarm, a safety-aware language-to-behavior-tree pipeline for generating XML behavior trees (BTs) from speech or text commands. The system combines multilingual translation, command-level safety filtering, constrained prompting, a LoRA-adapted large language model (LLM), and deterministic parser validation against a whitelist of executable swarm primitives. We evaluate eleven open 6.7B--14B parameter LLMs, all using 4-bit quantization, on representative swarm-control scenarios under zero-shot, one-shot, and two-shot prompting. Falcon3-Instruct-10B and Mistral-7B-v3 are the strongest prompt-engineered candidates, reaching BLEU scores above 0.60 and high syntactic validity in few-shot settings. LoRA adaptation of Falcon3-Instruct-10B on a 2,063-example synthetic instruction--BT corpus improves zero-shot BLEU from 0.267 to 0.663, ROUGE-L from 0.366 to 0.692, and parser-accepted syntactic validity from 0% to 72%. Translation experiments further show that SeamlessM4T v2-large and EuroLLM-9B provide the best quality-latency trade-offs for the multilingual front end. The results indicate that compact, quantized, domain-adapted LLMs can generate useful swarm BTs when embedded in a validated systems pipeline. They also show that parser acceptance and safety filtering remain necessary execution gates; generation quality alone is not sufficient for autonomous deployment.

Mohammed Majid, Amjad Yousef Majid

Multi-Robot Systems Swarm Robotics Large Language Model-Based Robot Planning LLM Safety Behavior Trees

May 4, 2026cs.RO

CoRAL: Contact-Rich Adaptive LLM-based Control for Robotic Manipulation

While Large Language Models (LLMs) and Vision-Language Models (VLMs) demonstrate remarkable capabilities in high-level reasoning and semantic understanding, applying them directly to contact-rich manipulation remains a challenge due to their lack of explicit physical grounding and inability to perform adaptive control. To bridge this gap, we propose CoRAL (Contact-Rich Adaptive LLM-based control), a modular framework that enables zero-shot planning by decoupling high-level reasoning from low-level control. Unlike black-box policies, CoRAL uses LLMs not as direct controllers, but as cost designers that synthesize context-aware objective functions for a sampling-based motion planner (MPPI). To address the ambiguity of physical parameters in visual data, we introduce a neuro-symbolic adaptation loop: a VLM provides semantic priors for environmental dynamics, such as mass and friction estimates, which are then explicitly refined in real time via online system identification, while the LLM iteratively modulates the cost-function structure to correct strategic errors based on interaction feedback. Furthermore, a retrieval-based memory unit allows the system to reuse successful strategies across recurrent tasks. This hierarchical architecture ensures real-time control stability by decoupling high-level semantic reasoning from reactive execution, effectively bridging the gap between slow LLM inference and dynamic contact requirements. We validate CoRAL on both simulation and real-world hardware across challenging and novel tasks, such as flipping objects against walls by leveraging extrinsic contacts. Experiments demonstrate that CoRAL outperforms state-of-the-art VLA and foundation-model-based planner baselines by boosting success rates over 50% on average in unseen contact-rich scenarios, effectively handling sim-to-real gaps through its adaptive physical understanding.

Berk Çiçek, Mert K. Er, Ozgur S. Oguz

LiRA Lab, Department of Computer Engineering, Bilkent University, Ankara, Türkiye

Language Model-Based Control Large Language Model-Based Robot Planning Contact-Rich Robotic Manipulation VLMs for Robotics

May 3, 2026cs.CV

TrajRAG: Retrieving Geometric-Semantic Experience for Zero-Shot Object Navigation

Existing zero-shot Object Goal Navigation (ObjectNav) methods often exploit commonsense knowledge from large language or vision-language models to guide navigation. However, such knowledge arises from internet-scale text rather than embodied 3D experience, and episodic observations collected during navigation are typically discarded, preventing the accumulation of lifelong experience. To this end, we propose Trajectory RAG (TrajRAG), a retrieval-augmented generation framework that enhances large-model reasoning by retrieving geometric-semantic experiences. TrajRAG incrementally accumulates episodic observations from past navigation episodes. To structure these observations, we propose a topological-polar (topo-polar) trajectory representation that compactly encodes spatial layouts and semantic contexts, effectively removing redundancies in raw episodic observations. A hierarchical chunking structure further organizes similar topo-polar trajectories into unified summaries, enabling coarse-to-fine retrieval. During navigation, candidate frontiers generate multiple trajectory hypotheses that query TrajRAG for similar past trajectories, guiding large-model reasoning for waypoint selection. New experiences are continually consolidated into TrajRAG, enabling the accumulation of lifelong navigation experience. Experiments on MP3D, HM3D-v1, and HM3D-v2 show that TrajRAG effectively retrieves relevant geometric-semantic experiences and improves zero-shot ObjectNav performance.

Yiyao Wang, Sixian Zhang, Keming Zhang +3

State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences, Beijing · University of Chinese Academy of Sciences, Beijing · Institute of Computing Technology, Chinese Academy of Sciences, Beijing

Embodied Navigation Large Language Model-Based Robot Planning Robot Navigation Object-Goal Navigation

May 2, 2026cs.RO

Assistance Without Interruption: A Benchmark and LLM-based Framework for Non-Intrusive Human-Robot Assistance

Human-robot interaction (HRI) has long studied how agents and people coordinate to achieve shared goals. In this work, we formalize and benchmark the non-intrusive assistance as an independent paradigm of HRI, where a robot proactively supports a human's ongoing multi-step activities while strictly avoiding interruptions. Unlike conventional HRI tasks that rely on direct commands, explicit negotiation, or proactive interventions based on user habits and history, our task treats the human's plan as the primary process and formulates assistance as a joint decision over when to act and what to do. To systematically evaluate this problem, we establish a simulation benchmark, NIABench, along with new metrics tailored to the non-intrusive assistance task. We further propose a hybrid architecture that integrates an LLM with a scoring model. The scoring model first applies semantic retrieval to prune large candidate action sets, and then a ranker evaluates human-step and robot-action pairs, enabling reasoning over timing and cross-step dependencies. Comprehensive experiments on both NIABench and real-world scenarios demonstrate that our method achieves proactive, non-intrusive assistance that reduces human effort while preserving task effectiveness.

Yuedi Zhang, Shuanghao Bai, Wanqi Zhou +4

Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, China · School of Electrical Engineering, Xi’an University of Technology, Xi’an, China

Large Language Model-Based Robot Planning Proactive Assistance Human-Robot Collaboration Human-Robot Interaction

Apr 29, 2026cs.CR

From Prompt to Physical Actuation: Holistic Threat Modeling of LLM-Enabled Robotic Systems

As large language models are integrated into autonomous robotic systems for task planning and control, compromised inputs or unsafe model outputs can propagate through the planning pipeline to physical-world consequences. Although prior work has studied robotic cybersecurity, adversarial perception attacks, and LLM safety independently, no existing study traces how these threat categories interact and propagate across trust boundaries in a unified architectural model. We address this gap by modeling an LLM-enabled autonomous robot in an edge-cloud architecture as a hierarchical Data Flow Diagram and applying STRIDE-per-interaction analysis across six boundary-crossing interaction points using a three-category taxonomy of Conventional Cyber Threats, Adversarial Threats, and Conversational Threats. The analysis reveals that these categories converge at the same boundary crossings, and we trace three cross-boundary attack chains from external entry points to unsafe physical actuation, each exposing a distinct architectural property: the absence of independent semantic validation between user input and actuator dispatch, cross-modal translation from visual perception to language-model instruction, and unmediated boundary crossing through provider-side tool use. To our knowledge, this is the first DFD-based threat analysis integrating all three threat categories across the full perception-planning-actuation pipeline of an LLM-enabled robotic system.

Neha Nagaraja, Hayretdin Bahsi, Carlo R. da Cunha

School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, USA · Department of Software Science, Tallinn University of Technology, Tallinn, Estonia

Large Language Model-Based Robot Planning Cybersecurity LLM Security

Apr 29, 2026cs.RO

LLM-Flax : Generalizable Robotic Task Planning via Neuro-Symbolic Approaches with Large Language Models

Deploying a neuro-symbolic task planner on a new domain today requires significant manual effort: a domain expert must author relaxation and complementary rules, and hundreds of training problems must be solved to supervise a Graph Neural Network (GNN) object scorer. We propose LLM-Flax, a three-stage framework that eliminates all three sources of manual effort using a locally hosted LLM given only a PDDL domain file. Stage 1 automatically generates relaxation and complementary rules via structured prompting with format validation and self-correction. Stage 2 introduces LLM-guided failure recovery with a feasibility-gated budget policy that explicitly reserves API latency cost before each LLM call, preventing the downstream relaxation fallback from being starved. Stage 3 replaces the domain-trained GNN entirely with zero-shot LLM object importance scoring, requiring no training data. We evaluate all three stages on the MazeNamo benchmark across 10x10, 12x12, and 15x15 grids (8 benchmarks total). LLM-Flax achieves average SR 0.945 versus the manual baseline's 0.828 (+0.117), matching or outperforming manual rules on every one of the eight benchmarks. On 12x12 Expert, LLM-Flax attains SR 0.733 where the manual planner fails entirely (SR 0.000); on 15x15 Hard, it achieves SR 1.000 versus Manual's 0.900. Stage 3 demonstrates feasibility (SR 0.720 on 12x12 Hard with no training data) but faces a context-window bottleneck at scale, pointing to the primary open challenge for future work.

Seongmin Kim, Daegyu Lee

Department of Physical AI Engineering, Jeonbuk National University(JBNU) · Faculty of Department of Advanced Defense Technology and Industry, Jeonbuk National University(JBNU)

Large Language Model-Based Robot Planning Symbolic Planning Symbolic Reasoning Robot Task Planning Neuro-Symbolic Reasoning

Apr 21, 2026cs.AI

SafetyALFRED: Evaluating Safety-Conscious Planning of Multimodal Large Language Models

Multimodal Large Language Models are increasingly adopted as autonomous agents in interactive environments, yet their ability to proactively address safety hazards remains insufficient. We introduce SafetyALFRED, built upon the embodied agent benchmark ALFRED, augmented with six categories of real-world kitchen hazards. While existing safety evaluations focus on hazard recognition through disembodied question answering (QA) settings, we evaluate eleven state-of-the-art models from the Qwen, Gemma, and Gemini families on not only hazard recognition, but also active risk mitigation through embodied planning. Our experimental results reveal a significant alignment gap: while models can accurately recognize hazards in QA settings, average mitigation success rates for these hazards are low in comparison. Our findings demonstrate that static evaluations through QA are insufficient for physical safety, thus we advocate for a paradigm shift toward benchmarks that prioritize corrective actions in embodied contexts. We open-source our code and dataset under https://github.com/sled-group/SafetyALFRED.git

Josue Torres-Fonseca, Naihao Deng, Yinpei Dai +5

University of Michigan · Boise State University

LLM Safety Benchmarks Large Language Model-Based Robot Planning AI Agent Safety AI Agent Benchmarks Robot Safety Multimodal Foundation Models+4

Apr 20, 2026cs.AI

Using large language models for embodied planning introduces systematic safety risks

Large language models are increasingly used as planners for robotic systems, yet how safely they plan remains an open question. To evaluate safe planning systematically, we introduce DESPITE, a benchmark of 12,279 tasks spanning physical and normative dangers with fully deterministic validation. Across 23 models, even near-perfect planning ability does not ensure safety: the best-planning model fails to produce a valid plan on only 0.4% of tasks but produces dangerous plans on 28.3%. Among 18 open-source models from 3B to 671B parameters, planning ability improves substantially with scale (0.4-99.3%) while safety awareness remains relatively flat (38-57%). We identify a multiplicative relationship between these two capacities, showing that larger models complete more tasks safely primarily through improved planning, not through better danger avoidance. Three proprietary reasoning models reach notably higher safety awareness (71-81%), while non-reasoning proprietary models and open-source reasoning models remain below 57%. As planning ability approaches saturation for frontier models, improving safety awareness becomes a central challenge for deploying language-model planners in robotic systems.

Tao Zhang, Kaixian Qu, Zhibin Li +4

ETH Zurich, Zurich, Switzerland. · University College London, London, United Kingdom. · Stanford University, Stanford, California, United States. +2

Large Language Model-Based Robot Planning Robot Safety Robot Task Planning LLM Safety Evaluation

Apr 17, 2026cs.RO

Semantic Area Graph Reasoning for Multi-Robot Language-Guided Search

Coordinating multi-robot systems (MRS) to search in unknown environments is particularly challenging for tasks that require semantic reasoning beyond geometric exploration. Classical coordination strategies rely on frontier coverage or information gain and cannot incorporate high-level task intent, such as searching for objects associated with specific room types. We propose \textit{Semantic Area Graph Reasoning} (SAGR), a hierarchical framework that enables Large Language Models (LLMs) to coordinate multi-robot exploration and semantic search through a structured semantic-topological abstraction of the environment. SAGR incrementally constructs a semantic area graph from a semantic occupancy map, encoding room instances, connectivity, frontier availability, and robot states into a compact task-relevant representation for LLM reasoning. The LLM performs high-level semantic room assignment based on spatial structure and task context, while deterministic frontier planning and local navigation handle geometric execution within assigned rooms. Experiments on the Habitat-Matterport3D dataset across 100 scenarios show that SAGR remains competitive with state-of-the-art exploration methods while consistently improving semantic target search efficiency, with up to 18.8% in large environments. These results highlight the value of structured semantic abstractions as an effective interface between LLM-based reasoning and multi-robot coordination in complex indoor environments.

Ruiyang Wang, Hao-Lun Hsu, Jiwoo Kim +1

Multi-Robot Systems Large Language Model-Based Robot Planning Multi-Robot Exploration Multi-Robot Coordination

Apr 16, 2026cs.RO

Foundation Models in Robotics: A Comprehensive Review of Methods, Models, Datasets, Challenges and Future Research Directions

Over the recent years, the field of robotics has been undergoing a transformative paradigm shift from fixed, single-task, domain-specific solutions towards adaptive, multi-function, general-purpose agents, capable of operating in complex, open-world, and dynamic environments. This tremendous advancement is primarily driven by the emergence of Foundation Models (FMs), i.e., large-scale neural-network architectures trained on massive, heterogeneous datasets that provide unprecedented capabilities in multi-modal understanding and reasoning, long-horizon planning, and cross-embodiment generalization. In this context, the current study provides a holistic, systematic, and in-depth review of the research landscape of FMs in robotics. In particular, the evolution of the field is initially delineated through five distinct research phases, spanning from the early incorporation of Natural Language Processing (NLP) and Computer Vision (CV) models to the current frontier of multi-sensory generalization and real-world deployment. Subsequently, a highly-granular taxonomic investigation of the literature is performed, examining the following key aspects: a) the employed FM types, including LLMs, VFMs, VLMs, and VLAs, b) the underlying neural-network architectures, c) the adopted learning paradigms, d) the different learning stages of knowledge incorporation, e) the major robotic tasks, and f) the main real-world application domains. For each aspect, comparative analysis and critical insights are provided. Moreover, a report on the publicly available datasets used for model training and evaluation across the considered robotic tasks is included. Furthermore, a hierarchical discussion on the current open challenges and promising future research directions in the field is incorporated.

Aggelos Psiris, Vasileios Argyriou, Evangelos K. Markakis +4

Department of Informatics and Telematics, Harokopio University of Athens, Athens, Greece · Department of Networks and Digital Media, Kingston University, London, United Kingdom · Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Heraklion, Greece +4

Large Language Model-Based Robot Planning Robot Foundation Models Cross-Embodiment Robot Learning Vision-Language-Action Models Multimodal Foundation Models VLMs for Robotics

Mar 25, 2026cs.RO

Object Search in Partially-Known Environments via LLM-informed Model-based Planning and Prompt Selection

We present a novel LLM-informed model-based planning framework, and a novel prompt selection method, for object search in partially-known environments. Our approach uses an LLM to estimate statistics about the likelihood of finding the target object when searching various locations throughout the scene that, combined with travel costs extracted from the environment map, are used to instantiate a model, thus using the LLM to inform planning and achieve effective search performance. Moreover, the abstraction upon which our approach relies is amenable to deployment-time model selection via the recent offline replay approach, an insight we leverage to enable fast prompt and LLM selection during deployment. Simulation experiments demonstrate that our LLM-informed model-based planning approach outperforms the baseline planning strategy that fully relies on LLM and optimistic strategy with as much as 11.8% and 39.2% improvements respectively, and our bandit-like selection approach enables quick selection of best prompts and LLMs resulting in 6.5% lower average cost and 33.8% lower average cumulative regret over baseline UCB bandit selection. Real-robot experiments in an apartment demonstrate similar improvements and so further validate our approach.

Abhishek Paudel, Abhish Khanal, Raihan I. Arnob +2

Department of Computer Science at George Mason University, Fairfax, Virginia, USA.

Large Language Model-Based Robot Planning Model-Based Planning

Mar 20, 2026cs.RO

IndoorR2X: Indoor Robot-to-Everything Coordination with LLM-Driven Planning

Although robot-to-robot (R2R) communication improves indoor scene understanding beyond what a single robot can achieve, R2R alone cannot overcome partial observability without substantial exploration overhead or scaling team size. In contrast, many indoor environments already include low-cost Internet of Things (IoT) sensors (e.g., cameras) that provide persistent, building-wide context beyond onboard perception. We therefore introduce IndoorR2X, a benchmark and simulation framework for Large Language Model (LLM)-driven multi-robot task planning with Robot-to-Everything (R2X) perception and communication in indoor environments. IndoorR2X integrates observations from mobile robots and static IoT devices to construct a global semantic state that supports scalable scene understanding, reduces redundant exploration, and enables high-level coordination through LLM-based planning. IndoorR2X provides configurable simulation environments, sensor layouts, robot teams, and task suites to systematically evaluate semantic-level coordination strategies. Extensive experiments across diverse settings demonstrate that IoT-augmented world modeling improves multi-robot efficiency and reliability, and we highlight key insights and failure modes for advancing LLM-based collaboration between robot teams and indoor IoT sensors. Project page: https://fandulu.github.io/IndoorR2X_project_page/.

Fan Yang, Soumya Teotia, Shaunak A. Mehta +8

1Fujitsu Research · 2Carnegie Mellon University

World Models for Robotics Large Language Model-Based Robot Planning Multi-Robot Coordination

Mar 19, 2026cs.RO

REST: Receding Horizon Explorative Steiner Tree for Zero-Shot Object-Goal Navigation

Zero-shot object-goal navigation (ZSON) requires navigating unknown environments to find a target object without task-specific training. Prior hierarchical solutions mainly focus on either scene understanding and representations (belief) or high-level decision-making and planning (policy), yet treat the option, i.e., the subgoal candidate that belief proposes and policy selects, as an interface inherited from adjacent modules rather than a design axis in its own right. In practice, options are predominantly single waypoints scored by destination utility: a lone destination hides the value gathered en route, and a flat list obscures the relationships among candidates. Our insight is that the option space should be a tree of paths. Full paths expose en-route information gain that destination-only scoring systematically neglects; a tree of shared segments enables coarse-to-fine LLM reasoning that dismisses or pursues entire branches before examining individual leaves, compressing the combinatorial path space into an efficient hierarchy. We instantiate this insight in REST (Receding Horizon Explorative Steiner Tree), a training-free framework that (1) builds an explicit open-vocabulary 3D map from online RGB-D streams; (2) grows an agent-centric tree of safe and informative paths as the option space via sampling-based planning; and (3) textualizes each branch into a spatial narrative and selects the next-best path through chain-of-thought LLM reasoning. Across the Gibson, HM3D, and HSSD benchmarks, REST consistently ranks among the top methods in success rate and path efficiency.

Shuqi Xiao, Maani Ghaffari, Chengzhong Xu +1

State Key Laboratory of Internet of Things for Smart City (SKL-IOTSC), University of Macau · Department of Naval Architecture and Marine Engineering, and Department of Robotics, University of Michigan

Zero-Shot Learning Large Language Model-Based Robot Planning Informative Path Planning Motion Planning Object-Goal Navigation

Mar 9, 2026cs.RO

MoMaStage: Skill-State Graph Guided Planning and Closed-Loop Execution for Long-Horizon Indoor Mobile Manipulation

Long-horizon indoor mobile manipulation (MoMa) requires robots to execute extended navigation-manipulation sequences whose feasibility depends on state changes induced by preceding skills. Vision-language models (VLMs) can decompose instructions into plausible skill sequences, but they do not reliably track such cumulative embodiment constraints or revise a plan when execution deviates from expectation. We present MoMaStage, a map-light framework for state-consistent planning and closed-loop execution in long-horizon indoor MoMa. MoMaStage couples a frozen VLM with robot execution through three mechanisms: (i) a hierarchical library of grounded, executable skills and a topology-only projection of a Skill-State Graph (SSG) that constrains the VLM's planning space; (ii) an SSG verifier that propagates scene-region and gripper-occupancy state to reject infeasible plans before execution; and (iii) an event-driven monitor that triggers graph-grounded repair only when an observed outcome invalidates the remaining plan. The SSG captures the compact embodiment state needed for skill sequencing without requiring a dense scene map, while geometric and contact-level conditions remain within the underlying controllers. Experiments in physics-rich simulation and on a real mobile manipulator show that MoMaStage improves planning validity and long-horizon execution survival over the evaluated baselines, while reducing latency and model token consumption.

Chenxu Li, Zixuan Chen, Yetao Li +5

All authors are with Nanjing University, China.

Long-Horizon Robotic Manipulation Mobile Manipulation Large Language Model-Based Robot Planning Robot Task Planning Model-Based Planning

Mar 6, 2026cs.RO

Multimodal Behavior Tree Generation: A Small Vision-Language Model for Robot Task Planning

Large language models have been widely used for robotic task planning, often taking advantage of representations such as Behavior Trees (BTs). Vision-Language Models (VLMs) have extended these works by grounding the generated plans in the observed scene. However, existing methods are either text-only or rely on large proprietary VLMs, while no dataset pairs visual observations and task instructions with executable and ROS2-compatible BTs. We address this gap with a multi-stage teacher pipeline that converts 1,622 Open X-Embodiment episodes into an augmented multimodal BT dataset containing 2,433 examples. We use this dataset to fine-tune compact and open-source VLMs, ranging from 500M to 4B parameters, using parameter-efficient fine-tuning (PEFT). We then evaluate the generated BTs offline in terms of syntactic correctness and by executing them on 15 household tasks in BEHAVIOR-1K. Our best model, Gemma-3 4B, achieves perfect BT validity and an 87% success rate, outperforming Claude Opus 4.8 and approaching GPT-5, while running locally. Finally, our ablation studies show that adding visual observations increases task success from 40% to 87%, while data augmentation increases BT validity from 65% to 100%.

Riccardo Andrea Izzo, Cristiano Battistini, Gianluca Bardaro +1

Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy

Large Language Model-Based Robot Planning Synthetic Data Generation for Robotics VLMs for Robotics Behavior Trees

Feb 23, 2026cs.RO

CoReLIN: Constraint-based Reasoning for Zero-shot Lifelong Interactive Navigation

Robot navigation typically assumes an obstacle-free path exists between start and goal. In real environments, however, clutter may block all routes. We introduce Lifelong Interactive Navigation, where a mobile robot with manipulation capabilities must move objects to forge paths and complete sequential object-placement tasks. Because environment modifications persist, decisions impact future navigability and task difficulty. We propose CoReLIN, an LLM-driven constraint-based reasoning framework with active perception. CoReLIN reasons over a structured scene graph to decide which objects to relocate, where to place them, and where to explore next. A standard motion planner executes reliable navigation and manipulation primitives. To evaluate long-horizon behavior, we introduce 2 new metrics - Long-term Efficiency Score (LES), a unified metric capturing success, execution efficiency, environment optimality, captured by Price of Clutter. In ProcTHOR-10k, CoReLIN outperforms best baseline by 16% under standard metrics and LES, and transfers to real-world hardware.

Apoorva Vashisth, Manav Kulshrestha, Pranav Bakshi +3

Purdue University · Purdue University, USA · IIT Kharagpur +5

Long-Horizon Robotic Manipulation Large Language Model-Based Robot Planning Symbolic Planning

Feb 16, 2026cs.RO

Replanning Human-Robot Collaborative Tasks with Vision-Language Models via Semantic and Physical Dual-Correction

Human-robot collaborative assembly requires robots to interpret ambiguous corrective instructions while producing physically executable motions. Vision-language models (VLMs) provide semantic reasoning but may select logically inconsistent targets or misjudge execution outcomes. We propose a replanning framework that maps human instructions to Action Target candidates, including grasp poses and tool selections, and combines an Internal Correction Model for pre-execution logical verification with an External Correction Model for post-execution visual verification. The framework integrates VLM reasoning with 6-DoF grasp generation and collision-free trajectory planning. Simulation ablations show configuration-dependent effects: internal correction improves candidate validity, whereas external correction enables recovery for a low-latency VLM but can reduce success when visual verification produces false negatives. Experiments with an upper-body humanoid robot achieved 66.7% success in real-world object fixation, 100% in initial tool selection, and 75.0% in corrective tool selection. These results demonstrate interactive replanning across spatial and semantic collaborative tasks while identifying visual-state verification as a key limitation.

Taichi Kato, Takuya Kiyokawa, Namiko Saito +1

aDepartment of Systems Science, School of Engineering Science, The University of Osaka, 1-3 Machikaneyama, Toyonaka, Osaka, Japan · bDepartment of Systems Innovation, Graduate School of Engineering Science, The University of Osaka, 1-3 Machikaneyama, Toyonaka, Osaka, Japan · cMicrosoft Research Asia, Shinagawa, Tokyo, Japan +1

Language-Conditioned Robot Manipulation Large Language Model-Based Robot Planning Human-Robot Collaboration Robotic Assembly VLMs for Robotics

Jan 28, 2026cs.RO

Demonstration-Free Robotic Control via LLM Agents

Robotic manipulation has increasingly adopted vision-language-action (VLA) models, which achieve strong performance but typically require task-specific demonstrations and fine-tuning, and often generalize poorly under domain shift. We investigate whether general-purpose large language model (LLM) agent frameworks, originally developed for software engineering, can serve as an alternative control paradigm for embodied manipulation. We introduce FAEA (Frontier Agent as Embodied Agent), which applies an LLM agent framework directly to embodied manipulation without modification. Using the same iterative reasoning that enables software agents to debug code, FAEA enables embodied agents to reason through manipulation strategies. We evaluate an unmodified frontier agent, Claude Agent SDK, across the LIBERO, ManiSkill3, and MetaWorld benchmarks. With privileged environment state access, FAEA achieves success rates of 84.9%, 85.7%, and 96%, respectively. This level of task success approaches that of VLA models trained with less than 100 demonstrations per task, without requiring demonstrations or fine-tuning. With one round of human feedback as an optional optimization, performance increases to 88.2% on LIBERO. This demonstration-free capability has immediate practical value: FAEA can autonomously explore novel scenarios in simulation and generate successful trajectories for training data augmentation in embodied learning. Our results indicate that general-purpose agents are sufficient for a class of manipulation tasks dominated by deliberative, task-level planning. This opens a path for robotics systems to leverage actively maintained agent infrastructure and benefit directly from ongoing advances in frontier models. Code is available at https://github.com/robiemusketeer/faea-sim

Brian Y. Tsui, Alan Y. Fang, Tiffany J. Hwu

All authors are independent researchers.

Robotic Control Language Model-Based Control Large Language Model-Based Robot Planning AI Control Robotic Manipulation

Jan 17, 2026cs.RO

Visual-Language-Guided Task Planning for Horticultural Robots

Crop monitoring is essential for precision agriculture, but current systems lack high-level reasoning. We introduce a novel, modular framework that uses a Vision Language Model (VLM) to guide robotic task planning by actively querying heterogeneous data sources, including enriched RGB camera feeds and 2D semantic occupancy maps, interleaved with robotic action primitives. We contribute a comprehensive benchmark for short- and long-horizon crop monitoring tasks in monoculture and polyculture environments. Our results show that while zero-shot VLMs perform robustly for short-horizon tasks (achieving 87% success, comparable to human experts), success drops significantly to under 10% for complex long-horizon, multi-target tasks. Despite this decline, task completion rates remain above 76% under noiseless conditions. Critically, the system degrades when relying on noisy semantic maps, demonstrating a key limitation in current VLM context grounding for sustained robotic operations. This work offers a deployable framework and critical insights into VLM capabilities and shortcomings for complex agricultural robotics.

Jose Cuaran, Kendall Koe, Aditya Potnis +2

The Siebel School of Computing and Data Science, University of Illinois, Urbana-Champaign · National Center for Supercomputing Applications at University of Illinois, Urbana-Champaign · The Department of Agricultural and Biological Engineering, University of Illinois, Urbana-Champaign

Large Language Model-Based Robot Planning Vision-Language Grounding Agricultural Robotics VLMs for Robotics Precision Agriculture

Sep 29, 2025cs.RO

Prompting Robot Teams with Natural Language

This paper presents a framework to prompt multi-robot teams with high-level tasks using natural language expressions. Our objective is to use the reasoning capabilities of language models in understanding and decomposing multi-robot collaboration and decision-making tasks, but in settings where such models cannot be called at deployment time. However, it is hard to specify the behavior of an individual robot from a team instruction, and have it continuously adapt to actions from other robots. This necessitates a framework with the representational capacity required by the logic and semantics of a task, and yet supports decentralized, real-time operation. We solve this dilemma by recognizing that a task can be represented as a deterministic finite automaton, and that recurrent neural networks (RNNs) can encode numerous automata. This allows us to distill the logic and sequential decompositions of sub-tasks obtained from a language model into an RNN, and align its internal states with the semantics of a given task. This leads to a tiny model that encapsulates the reasoning of the language model and can be implemented onboard. To interpret the internal state of the RNN for a decentralized execution, we train a graph neural network control policy conditioned on the hidden states of the RNN and the language embeddings. We present evaluations on simulated and real-world multi-robot tasks that require sequential and collaborative behavior by the team, demonstrating scalable, robust, real-time performance -- sites.google.com/view/prompting-teams.

Eduardo Sebastián, Nicolas Pfitzer, Ajay Shankar +1

Department of Computer Science and Technology, University of Cambridge, UK · Department of Mechanical and Process Engineering, ETH Zurich, Switzerland

Large Language Model-Based Robot Planning Multi-Robot Coordination Language-Conditioned Robot Control

Sep 25, 2025cs.RO

Self-CriTeach: LLM Self-Teaching and Self-Critiquing for Improving Robotic Planning via Automated Domain Generation

Large Language Models (LLMs) have recently shown strong promise for robotic task planning, particularly through automatic planning domain generation. However, prior approaches largely treat generated planning domains as planning utilities, which are brittle under imperfect logical states and perception noise, overlooking their potential as scalable sources of reasoning supervision and structured reward signals. At the same time, reasoning LLMs depend on chain-of-thought (CoT) supervision that is expensive to collect for robotic tasks, and reinforcement learning (RL) faces challenges in reward engineering. We propose Self-CriTeach, an LLM self-teaching and self-critiquing framework in which an LLM autonomously generates symbolic planning domains that serve a dual role: (1) enabling large-scale generation of robotic planning problem-plan pairs, and (2) providing structured reward functions. First, the self-written domains enable large-scale generation of symbolic task plans, which are automatically transformed into extended CoT trajectories for supervised fine-tuning. Second, the self-written domains are reused as structured reward functions, providing dense feedback for reinforcement learning without manual reward engineering. This unified training pipeline yields a planning-enhanced LLM with higher planning success rates, stronger cross-task generalization, reduced inference cost, and resistance to imperfect logical states. GitHub Page: https://markli1hoshipu.github.io/Plan_LLM/

Jinbang Huang, Zhiyuan Li, Yuanzhao Hu +4

Huawei Noah’s Ark Lab · University of Toronto · University of British Columbia +1

Large Language Model-Based Robot Planning Synthetic Data Generation for Robotics LLM-Guided RL Robot Task Planning