arXiv AI
APTree: Extended Behavior Trees for Adaptive and Scalable Automated Robotic Task Planning in Construction
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
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
Sep 1
Scale-Plan: Scalable Language-Enabled Task Planning for Heterogeneous Multi-Robot Teams
arXiv:2603.08814v2 Announce Type: replace-cross Abstract: Long-horizon task planning for heterogeneous multi-robot systems is essential for deploying collaborative teams in real-world environments; y...
arXiv AI
2d ago
SLoSa: Small, Low-Power, and Safe Foundation Models for Robotic Task Planning
SLoSa is a neuro‑symbolic framework that compresses and fine‑tunes foundation models to create small, low‑power, and safety‑aware robotic task planners. It builds a safety‑aware dataset using classical planners, a teacher LLM, and formal verifiers, then applies supervised and reinforcement‑learning fine‑tuning guided by a verification‑based reward machine. Evaluations on PDDL domains, household simulators, and a desktop robotic arm show that verifier‑guided post‑training boosts success rates to 70.5–76.0% and that INT4 deployment on a Jetson Orin consumes only 17.85–26.41 W of power.
arXiv AI
Jul 1
Embodied CAD: Solver-Grounded LLM Agents for Parametric B-Rep Assembly Modeling
arXiv:2606. 31252v1 Announce Type: new Abstract: Large language models can write plausible CAD scripts, but reliable industrial CAD modeling requires more than syntactically valid code: every feature, placement, and assembly relation must be accepted by an exact geometric kernel while remaining editable as parametric boundary representation geometry.
arXiv AI
Aug 18
PDDLCoder: Agentic PDDL Generation for LLM-Assisted Symbolic Planning
arXiv:2608. 16637v1 Announce Type: new Abstract: LLMs remain unreliable for long-horizon planning, often generating logically inconsistent or non-applicable plans.
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.