Heterogeneous multi-robot systems are increasingly deployed in long-horizon missions that require coordination among robots with diverse capabilities. However, existing planning approaches struggle to construct accurate symbolic representations and maintain plan consistency in dynamic environments. Classical PDDL planners require manually crafted symbolic models, while LLM-based planners often ignore agent heterogeneity and environmental uncertainty. We introduce KGLAMP, a knowledge-graph-guided LLM planning framework for heterogeneous multi-robot teams. The framework maintains a structured knowledge graph encoding object relations, spatial reachability, and robot capabilities, which guides the LLM in generating accurate PDDL problem specifications. The knowledge graph serves as a persistent, dynamically updated memory that incorporates new observations and triggers replanning upon detecting inconsistencies, enabling symbolic plans to adapt to evolving world states. Experiments on the MAT-THOR benchmark show that KGLAMP improves performance by at least 25.5% over both LLM-only and PDDL-based variants.
We study a dynamic version of multi-agent path finding problem (called D-MAPF) where existing agents may leave and new agents may join the team at different times. We introduce a new method to solve D-MAPF based on conflict-resolution. The idea is, when a set of new agents joins the team and there are conflicts, instead of replanning for the whole team, to replan only for a minimal subset of agents whose plans conflict with each other. We utilize answer set programming as part of our method for planning, replanning and identifying minimal set of conflicts.
Carnegie Mellon University（卡内基梅隆大学）；Technion-Israel Institute of Technology（以色列理工学院）
In warehouse and manufacturing environments, manipulation platforms are frequently deployed at conveyor belts to perform pick and place tasks. Because objects on the conveyor belts are moving, robots have limited time to pick them up. This brings the requirement for fast and reliable motion planners that could provide provable real-time planning guarantees, which the existing algorithms do not provide. Besides the planning efficiency, the success of manipulation tasks relies heavily on the accuracy of the perception system which is often noisy, especially if the target objects are perceived fr…
Carnegie Mellon University（卡内基梅隆大学）；Technion-Israel Institute of Technology（以色列理工学院）
In warehouse and manufacturing environments, manipulation platforms are frequently deployed at conveyor belts to perform pick and place tasks. Because objects on the conveyor belts are moving, robots have limited time to pick them up. This brings the requirement for fast and reliable motion planners that could provide provable real-time planning guarantees, which the existing algorithms do not provide. Besides the planning efficiency, the success of manipulation tasks relies heavily on the accuracy of the perception system which is often noisy, especially if the target objects are perceived fr…
OceanPlan: Hierarchical Planning and Replanning for Natural Language AUV Piloting in Large-scale Unexplored Ocean Environments
School of Electrical and Computer Engineering, Georgia Institute of Technology（佐治亚理工学院电气与计算机工程学院）；The Hong Kong University of Science and Technology（香港科技大学）；School of Electrical Engineering, University of Notre Dame（圣母大学电气工程学院）
LLM-Grounded Dynamic Task Planning with Hierarchical Temporal Logic for Human-Aware Multi-Robot Handover
The Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳人工智能与机器人社会研究院）；Harbin Institute of Technology（哈尔滨工业大学）；The Chinese University of Hong Kong-Shenzhen（香港中文大学（深圳））；Tsinghua Shenzhen International Graduate School（清华大学深圳国际 Graduate School）
Georgia Institute of Technology（佐治亚理工学院）；University of Washington（华盛顿大学）
We present an incremental search algorithm, called Lifelong-GLS, which combines the vertex efficiency of Lifelong Planning A* (LPA*) and the edge efficiency of Generalized Lazy Search (GLS) for efficient replanning on dynamic graphs where edge evaluation is expensive. We use a lazily evaluated LPA* to repair the cost-to-come inconsistencies of the relevant region of the current search tree based on the previous search results, and then we restrict the expensive edge evaluations only to the current shortest subpath as in the GLS framework. The proposed algorithm is complete and correct in findi…
Korea Advanced Institute of Science and Technology（韩国科学技术院）
We present a task-and-motion planning (TAMP) algorithm robust against a human operator's cooperative or adversarial interventions. Interventions often invalidate the current plan and require replanning on the fly. Replanning can be computationally expensive and often interrupts seamless task execution. We introduce a dynamically reconfigurable planning methodology with behavior tree-based control strategies toward reactive TAMP, which takes the advantage of previous plans and incremental graph search during temporal logic-based reactive synthesis. Our algorithm also shows efficient recovery fu…
SLAP: Simultaneous Localization and Planning Under Uncertainty for Physical Mobile Robots via Dynamic Replanning in Belief Space: Extended version
NASA-JPL, Caltech（NASA-喷气推进中心，加州理工学院）；Dept. of Aerospace Eng. and Amato is with the Dept. of Computer Science（航空航天工程系和计算机科学系）；Dept. of Computer Science（计算机科学系）
LLM-Based Generalizable Hierarchical Task Planning and Execution for Heterogeneous Robot Teams with Event-Driven Replanning
Department of Mechanical Engineering, IIT Gandhinagar（印度加尔各直辖区理工学院机械工程系）；Sahyadri College of Engineering and Management（萨哈亚德里工程与管理学院）；Vishwakarma Institute of Information Technology（维什瓦克arma信息技术学院）
Revisiting Replanning from Scratch: Real-Time Incremental Planning with Fast Almost-Surely Asymptotically Optimal Planners
Estimation, Search, and Planning (ESP) Research Group, Queen’s University（估计、搜索与规划研究组，女王大学）；CoMMA Lab, Department of Computer Science, Purdue University（CoMMA实验室，计算机科学系，普渡大学）
University of Maryland at College Park（马里兰大学帕克分校）；University of Maryland（马里兰大学）
We present a decentralized path-planning algorithm for navigating multiple differential-drive robots in dense environments. In contrast to prior decentralized methods, we propose a novel congestion metric-based replanning that couples local and global planning techniques to efficiently navigate in scenarios with multiple corridors. To handle dense scenes with narrow passages, our approach computes the initial path for each agent to its assigned goal using a lattice planner. Based on neighbors' information, each agent performs online replanning using a congestion metric that tends to reduce the…
Fast, autonomous flight in unstructured, cluttered environments such as forests is challenging because it requires the robot to compute new plans in realtime on a computationally-constrained platform. In this paper, we enable this capability with a search-based planning framework that adapts sampling density in realtime to find dynamically-feasible plans while remaining computationally tractable. A paramount challenge in search-based planning is that dense obstacles both necessitate large graphs (to guarantee completeness) and reduce the efficiency of graph search (as heuristics become less ac…
Hong Kong University of Science and Technology（香港科技大学）；Massachusetts Institute of Technology（麻省理工学院）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）；Carnegie Mellon University（卡内基梅隆大学）；UMass Amherst（马萨诸塞大学阿默斯特分校）
Self-Evolutionary Replanning for Failure-Aware Motion Planning
Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系）；SIAT, Chinese Academy of Sciences（中国科学院上海技术物理研究所）；Department of Computer Science, University of Hong Kong（香港大学计算机科学系）；Academy for Engineering & Technology, Fudan University（复旦大学工程与技术学院）；Department of EEE, Southern University of Science and Technology（南方科技大学电子工程系）
Integrated Exploration and Sequential Manipulation on Scene Graph with LLM-based Situated Replanning
Beihang University（北航大学）；State Key Laboratory of General Artificial Intelligence（一般人工智能国家重点实验室）；University of California, Los Angeles（加州大学洛杉矶分校）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）
Video Replanning via Latent Embedding Refinement and Rejection-Based Sampling
National Taiwan University（国立台湾大学）；MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）；Cornell University（康奈尔大学）；Harvard University（哈佛大学）；Department of XXX, University of YYY, Location, Country（XXX系，YYY大学，地点，国家）；School of ZZZ, Institute of WWW, Location, Country（ZZZ学院，WWW研究所，地点，国家）
Robot navigation and target capturing using nature-inspired approaches in a dynamic environment
ABV-Indian Institute of Information Technology and Management, Gwalior（阿塔尔·比哈里·瓦杰帕伊印度信息技术与管理学院，瓜廖尔）；Indian Institute of Information Technology, Pune（印度信息技术学院，浦那）
In an environment where a manipulator needs to execute multiple consecutive tasks, the act of object manoeuvre will change the underlying configuration space, affecting all subsequent tasks. Previously free configurations might now be occupied by the manoeuvred objects, and previously occupied space might now open up new paths. We propose Lazy Tree-based Replanner (LTR*) -- a novel hybrid planner that inherits the rapid planning nature of existing anytime incremental sampling-based planners. At the same time, it allows subsequent tasks to leverage prior experience via a lazy experience graph. …
GraphThink: Graph-Enhanced LLM Thinking for Long-Horizon Embodied Task Planning
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；Tsinghua University（清华大学）；The Hong Kong Polytechnic University（香港理工大学）
Modular Multi-Level Replanning TAMP Framework for Dynamic Environment
Research Center of the Satellite Technology, Harbin Institute of Technology（哈尔滨工业大学卫星技术研究中心）；Institute of Space Science and Applied Technology, Harbin Institute of Technology Shenzhen（哈尔滨工业大学深圳校区空间科学与应用技术研究院）
APTree: Extended Behavior Trees for Adaptive and Scalable Automated Robotic Task Planning in Construction
University of Stuttgart（斯图加特大学）；Institute for Computational Design and Construction（计算设计与建造研究所）；Institute for Control Engineering of Machine Tools and Manufacturing Units（机床与制造单元控制工程研究所）
AdaReP:Adaptive Re-Planning under Model Mismatch for Neural World-Model Predictive Control
Shanghai Jiao Tong University（上海交通大学）；Beijing Institute for General Artificial Intelligence (BIGAI)（北京通用人工智能研究院）；University of Science and Technology of China（中国科学技术大学）
