PIE-APT introduces a unified framework for abductive planning over Temporal Dynamic Knowledge Graphs (TDKGs) using two modules: PIE-Abducer, which performs incremental direct-derivation abduction, and PIE-APT, which interleaves backward‑chaining A* search with PIE-Abducer to generate action sequences and abductive assumptions. The approach operates natively on the expressive SROIQ Description Logic, leveraging an incremental reasoner to maintain decidability and bypass the Ramification Problem. Evaluation on four OWL benchmarks demonstrates qualitative superiority over classical planners and shows that the direct‑derivation method outperforms a Minimal Hitting Set baseline in abductive enrichment.
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.
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.
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.
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.
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.
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.
arXiv:2508.01300v2 Announce Type: replace Abstract: AI planning provides agents the ability to act autonomously towards achieving a goal in a given environment. Recent advances in Large Language Mode...
arXiv:2510. 00182v2 Announce Type: replace-cross Abstract: While we know that large language models (LLMs) can solve some planning problems, we do not understand the extent of these capabilities for robotics.
MemPlan is a memory‑conditioned PDDL planning framework designed for partially observable interactive text environments. It keeps the PDDL domain fixed and reconstructs the problem at each step using parsed observations, candidate hypotheses, and interaction memory, employing positive graph memory to assign relevance‑based action costs and negative memory to record refuted assumptions as exclusion constraints. Experiments on TextWorld, ALFWorld, and Robotouille demonstrate that MemPlan improves task completion and step efficiency over language‑only and PDDL‑aware baselines while significantly reducing online token cost compared with LLM‑planning baselines such as ReAct, and its fine‑tuned interface generalizes across task types without task‑specific fine‑tuning.
The paper reviews and categorizes large language models that combine step‑by‑step reasoning with interactive search, treating retrieval as an action within an iterative agentic loop. It analyzes these systems along four architectural axes—retrieval coupling, controller design, knowledge source, and grounding strictness—while also considering the learning signal as a cross‑cutting factor. The authors conduct controlled ablations and benchmark analyses to clarify how design choices affect search, stopping, and grounding behavior, aiming to provide a unified perspective for future research.
arXiv:2607. 04854v1 Announce Type: new Abstract: Despite their strong reasoning capabilities and extensive world knowledge, Large Language Models (LLMs) frequently generate plans that violate task constraints, undermining their reliability in real-world applications.
arXiv:2608. 13678v1 Announce Type: cross Abstract: A central goal of robot learning is to enable robots to execute rich instructions specified at runtime.