arXiv AI
OrbitTAMP: Grounding Language Models for Task and Motion Planning in Spacecraft Rendezvous
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arXiv AI
Jun 4
Semantic Constraint Synthesis for Adaptive Trajectory Optimization via Large Language Models
arXiv:2606. 04123v1 Announce Type: cross Abstract: Trajectory optimization is a critical component for enabling safe and reliable autonomous operations in space exploration.
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
2d ago
AstroAgentBench: Evaluating Agentic Planning on Space Mission Planning Tasks
arXiv:2601.11354v2 Announce Type: replace Abstract: Recent LLM-for-Space systems address mission planning, scheduling, operations support, simulator control, and autonomy, but their evaluations use d...
arXiv AI
Jun 18
As You Wish: Mission Planning with Formal Verification using LLMs in Precision Agriculture
arXiv:2606. 18519v1 Announce Type: cross Abstract: Though robotic systems are now being commercialized and deployed in various industries, many of these systems are highly specialized and often require an advanced skill set to operate and ensure they perform as instructed.
arXiv AI
Jul 20
A Systematic Study of Large Language Models for Task and Motion Planning With PDDLStream
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.
arXiv AI
Aug 25
Physical Agentic AI: An Architecture for Orchestrating a Robot Crew with LLMs
Physical Agentic AI proposes an architecture that links semantic planning with physical execution for robot crews. Each robot exposes a typed skill library, while a foundation model planner decomposes tasks into phases and assigns robot‑skill pairs. A Robot Orchestrator validates and authorizes one skill at a time, ensuring actions are grounded in robot capabilities, system state, and workflow constraints before actuation.