GAVEL: Graph World Models for Verified and Efficient Long-Horizon LLM Task Planning
cs.RO, cs.AI
Submitted: 2026-09-16
Updated: 2026-09-16
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
- Inner Monologue: Embodied Reasoning through Planning with Language Models
- LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks
- SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning
- Integrated Exploration and Sequential Manipulation on Scene Graph with LLM-based Situated Replanning
- SEEK: Semantic Reasoning for Object Goal Navigation in Real World Inspection Tasks
- COMRES-VLM: Coordinated Multi-Robot Exploration and Search using Vision Language Models
- Partially Observable Task and Motion Planning with Uncertainty and Risk Awareness
- ProgPrompt: Generating Situated Robot Task Plans using Large Language Models
- LLM+P: Empowering Large Language Models with Optimal Planning Proficiency
- Trust the PRoC3S: Solving Long-Horizon Robotics Problems with LLMs and Constraint Satisfaction
- Graph World Model
- AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agents
- VeriGraph: Scene Graphs for Execution Verifiable Robot Planning
- LookPlanGraph: Embodied Instruction Following Method with VLM Graph Augmentation
Related papers
- FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
- MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
- RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
- HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
- Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving