Papers Task Planning
“Task Planning” 태그가 달린 논문 344편 · 필터 해제
RoboBrain: A Unified Brain Model for Robotic Manipulation from Abstract to Concrete
Recent advancements in Multimodal Large Language Models (MLLMs) have shown remarkable capabilities across various multimodal contexts. However, their application in robotic scenarios, particularly for long-horizon manipu…
Task PlanningTrajectory PredictionStructured Preference Optimization for Vision-Language Long-Horizon Task Planning
Existing methods for vision-language task planning excel in short-horizon tasks but often fall short in complex, long-horizon planning within dynamic environments. These challenges primarily arise from the difficulty of …
Task PlanningVisual GroundingMRBTP: Efficient Multi-Robot Behavior Tree Planning and Collaboration
Multi-robot task planning and collaboration are critical challenges in robotics. While Behavior Trees (BTs) have been established as a popular control architecture and are plannable for a single robot, the development of…
Robot Task PlanningTask PlanningRapidPen: Fully Automated IP-to-Shell Penetration Testing with LLM-based Agents
We present RapidPen, a fully automated penetration testing (pentesting) framework that addresses the challenge of achieving an initial foothold (IP-to-Shell) without human intervention. Unlike prior approaches that focus…
Task PlanningPlan-over-Graph: Towards Parallelable LLM Agent Schedule
Large Language Models (LLMs) have demonstrated exceptional abilities in reasoning for task planning. However, challenges remain under-explored for parallel schedules. This paper introduces a novel paradigm, plan-over-gra…
Task PlanningTowards Robust and Secure Embodied AI: A Survey on Vulnerabilities and Attacks
Embodied AI systems, including robots and autonomous vehicles, are increasingly integrated into real-world applications, where they encounter a range of vulnerabilities stemming from both environmental and system-level f…
Adversarial AttackAutonomous VehiclesDecision MakingMotion Planning+2Scaling Autonomous Agents via Automatic Reward Modeling And Planning
Large language models (LLMs) have demonstrated remarkable capabilities across a range of text-generation tasks. However, LLMs still struggle with problems requiring multi-step decision-making and environmental feedback, …
Decision MakingMathematical Problem-SolvingNavigateTask Planning+1OctoTools: An Agentic Framework with Extensible Tools for Complex Reasoning
Solving complex reasoning tasks may involve visual understanding, domain knowledge retrieval, numerical calculation, and multi-step reasoning. Existing methods augment large language models (LLMs) with external tools but…
MedQAMMLUTask PlanningNavRAG: Generating User Demand Instructions for Embodied Navigation through Retrieval-Augmented LLM
Vision-and-Language Navigation (VLN) is an essential skill for embodied agents, allowing them to navigate in 3D environments following natural language instructions. High-performance navigation models require a large amo…
NavigateRAGRetrievalRetrieval-augmented Generation+3D-CIPHER: Dynamic Collaborative Intelligent Multi-Agent System with Planner and Heterogeneous Executors for Offensive Security
Large Language Models (LLMs) have been used in cybersecurity such as autonomous security analysis or penetration testing. Capture the Flag (CTF) challenges serve as benchmarks to assess automated task-planning abilities …
Task PlanningSTMA: A Spatio-Temporal Memory Agent for Long-Horizon Embodied Task Planning
A key objective of embodied intelligence is enabling agents to perform long-horizon tasks in dynamic environments while maintaining robust decision-making and adaptability. To achieve this goal, we propose the Spatio-Tem…
Decision MakingSpatial ReasoningTask Planning3D-Grounded Vision-Language Framework for Robotic Task Planning: Automated Prompt Synthesis and Supervised Reasoning
Vision-language models (VLMs) have achieved remarkable success in scene understanding and perception tasks, enabling robots to plan and execute actions adaptively in dynamic environments. However, most multimodal large l…
Code GenerationScene UnderstandingSmall Language ModelTask PlanningVote-Tree-Planner: Optimizing Execution Order in LLM-based Task Planning Pipeline via Voting
Integrating large language models (LLMs) into closed-loop robotic task planning has become increasingly popular within embodied artificial intelligence. Previous efforts mainly focused on leveraging the strong reasoning …
Decision MakingTask PlanningLarge Language Models for Multi-Robot Systems: A Survey
The rapid advancement of Large Language Models (LLMs) has opened new possibilities in Multi-Robot Systems (MRS), enabling enhanced communication, task planning, and human-robot interaction. Unlike traditional single-robo…
Action GenerationBenchmarkingHallucinationMathematical Reasoning+3Robotouille: An Asynchronous Planning Benchmark for LLM Agents
Effective asynchronous planning, or the ability to efficiently reason and plan over states and actions that must happen in parallel or sequentially, is essential for agents that must account for time delays, reason over …
Language ModelingLanguage ModellingLarge Language ModelTask PlanningA Schema-Guided Reason-while-Retrieve framework for Reasoning on Scene Graphs with Large-Language-Models (LLMs)
Scene graphs have emerged as a structured and serializable environment representation for grounded spatial reasoning with Large Language Models (LLMs). In this work, we propose SG-RwR, a Schema-Guided Retrieve-while-Reas…
HallucinationSpatial ReasoningTask Planning3D-MoE: A Mixture-of-Experts Multi-modal LLM for 3D Vision and Pose Diffusion via Rectified Flow
3D vision and spatial reasoning have long been recognized as preferable for accurately perceiving our three-dimensional world, especially when compared with traditional visual reasoning based on 2D images. Due to the dif…
Instruction FollowingMixture-of-ExpertsQuestion AnsweringSpatial Reasoning+2PhysBench: Benchmarking and Enhancing Vision-Language Models for Physical World Understanding
Understanding the physical world is a fundamental challenge in embodied AI, critical for enabling agents to perform complex tasks and operate safely in real-world environments. While Vision-Language Models (VLMs) have sh…
BenchmarkingCommon Sense ReasoningScene UnderstandingTask PlanningZero-shot Robotic Manipulation with Language-guided Instruction and Formal Task Planning
Robotic manipulation is often challenging due to the long-horizon tasks and the complex object relationships. A common solution is to develop a task and motion planning framework that integrates planning for high-level t…
Motion PlanningTask and Motion PlanningTask PlanningWorld KnowledgeDivide-Then-Aggregate: An Efficient Tool Learning Method via Parallel Tool Invocation
Although current Large Language Models (LLMs) exhibit impressive capabilities, performing complex real-world tasks still requires tool learning. Mainstream methods, such as CoT/ReAct, rely on step-by-step tool invocation…
Task Planning