arXiv AI
Scale-Plan: Scalable Language-Enabled Task Planning for Heterogeneous Multi-Robot Teams
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arXiv AI
Sep 18
GAVEL: Graph World Models for Verified and Efficient Long-Horizon LLM Task Planning
GAVEL is a framework that uses an explicit graph world model to verify and repair long‑horizon plans generated by large language models (LLMs). The graph encodes object relations, action pre‑conditions and effects, and probabilistic beliefs about unobserved object locations, allowing the system to predict action outcomes, detect violations, and repair them before execution. In experiments on BEHAVIOR‑1K, GAVEL boosts single‑task success from 41.2 % to 91.8 % and multi‑task success from 19.9 % to 92.6 %, while also reducing travel distance by about 5.4 % compared with a static variant.
arXiv AI
Jun 2
DAG-Plan: Generating Directed Acyclic Dependency Graphs for Dual-Arm Cooperative Planning
arXiv:2406. 09953v4 Announce Type: replace-cross Abstract: Dual-arm robots promise greater efficiency but require planning for complex tasks with nonlinear sub-task dependencies.
arXiv AI
Aug 3
Combining Large Language Models and Symbolic Reasoning for Multi-Robot Temporal Planning through Explainable Knowledge Bases
arXiv:2502. 19135v2 Announce Type: replace Abstract: We present PLANTOR, a framework for generating and executing multi-robot task plans from natural-language task descriptions through LLM-assisted knowledge-base construction.
arXiv AI
Aug 28
STEP: State-Aware Task Estimation and Planning with Multi-Modal LLMs for Human-Robot Collaboration
The paper introduces STEP, a State‑Aware Task Estimator and Planner that uses multi‑modal large language models to explicitly estimate system states and predict state transitions during task planning. By forecasting future states alongside actions, STEP reduces hallucinated actions and improves task‑convergent planning. In a simulated robot assembly task, STEP outperforms the state‑of‑the‑art by 32.8% in action executability and 14.8% in final‑state error.
arXiv AI
Jul 24
Vision-Language-Policy Model for Dynamic Robot Task Planning
arXiv:2512. 19178v2 Announce Type: replace-cross Abstract: Bridging the gap between natural language commands and autonomous execution in unstructured environments remains an open challenge for robotics.
arXiv AI
Aug 11
REMAC: Self-Reflective and Self-Evolving Multi-Agent Collaboration for Long-Horizon Robot Manipulation
arXiv:2503. 22122v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) have demonstrated remarkable capabilities in robotic planning, particularly for long-horizon tasks that require a holistic understanding of the environment for task decomposition.