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.
By Enrico Saccon, Matteo Saveriano, Edoardo Lamon, Luigi Palopoli, Marco Roveri
The paper introduces the Event Reasoning System (ERS), a framework that represents reasoning as an explicit event graph and uses hierarchical rule composition to manage dependencies and revisions. ERS employs semantic features to nominate candidate objects and rule bindings, while low‑level rules provide reusable transformations and high‑level control rules organize subgoals, ensuring that only admissible operations enter accepted reasoning paths. Empirical results on structured‑event graphs show ERS can build long dependency chains, eliminate irrelevant operations, and maintain correctness across numerous test cases, while reducing inference time by over 50% compared to single‑level control.
By Cheng Lyu, Xueqi Wang
arXiv:2501. 18784v5 Announce Type: replace Abstract: Heuristics are a central component of deterministic planning, particularly in domain-independent settings where general applicability is prioritized over task-specific tuning.
By Alexander Tuisov, Yonatan Vernik, Alexander Shleyfman
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.
By Ruiyang Wang, Hao-Lun Hsu, Swarajh Mehta, Jiwoo Kim, Zhihao Dou, Miroslav Pajic
OrbitTAMP introduces a hierarchical framework that grounds large language model (LLM) reasoning in spacecraft task-and-motion planning (TAMP) for rendezvous and proximity operations. The system maps natural‑language commands to partial mission specifications, resolves unspecified decisions with domain‑specific planners, and then optimizes trajectories for dynamic feasibility. Experiments show significant improvements in intent recovery, achieving 98% exact recovery with frontier LLMs and enhancing performance further with verifier‑guided revision and behavior‑plan search.
By Yuji Takubo, Daniele Gammelli, Marco Pavone, Simone D'Amico
The paper examines the limitations of using large language models (LLMs) to translate natural language planning problems into PDDL representations, noting that standard benchmarks often map directly to PDDL and may overstate success. It introduces the concept of "unraveling problems," where concise natural language descriptions correspond to large PDDL structures, and shows that traditional LLM formalizers struggle to scale on such variants. To address this, the authors propose a new paradigm, LLM-as-Higher-Order-Formalizer, where the LLM first generates a high‑level program that encapsulates recurrent logic, which then produces the full PDDL representation, thereby decoupling token output from combinatorial explosion and improving performance on complex tasks.
By Owen Jiang, Cassie Huang, Ashish Sabharwal, Li Zhang