MultiUAV-Plat supplies a new RESTful simulation platform and 1500-task benchmark where Agent4Drone reaches 57.9% task pass rate versus 30.6% for ReAct baseline across 75 multi-UAV missions.
SMART-LLM: Smart Multi-Agent Robot Task Planning Using Large Language Mod- els
8 Pith papers cite this work, alongside 11 external citations. Polarity classification is still indexing.
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Co-GLANCE distills vision-language models into an end-to-end onboard model for occlusion segmentation and robot allocation, using conformal prediction plus selective abstention to trigger active perception and achieve 25-36% higher accuracy with 350x lower latency than cloud baselines.
PerceptTwin creates interactive simulations from open-vocabulary object maps for verifying and refining LLM robot plans, reporting ~39% higher success rates and up to 18% better human verification.
MAVIC corrects Bellman backups at instruction boundaries by adjusting the incoming objective and restoring continuation value, enabling consistent estimation under stochastic instruction switching in cooperative MARL.
Thought-Retriever retrieves filtered and organized thoughts from past queries to let LLMs handle arbitrarily long external knowledge without context-length limits.
A 133M-parameter ensemble of fine-tuned mpnet and MiniLM encoders achieves 83.5% accuracy on a 200-task synthetic benchmark for robot skill prediction, beating several larger zero-shot LLMs.
A survey that categorizes LLM uses in multi-robot systems across task allocation, motion planning, action generation, and human interaction, while noting challenges and future research opportunities.
citing papers explorer
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MultiUAV-Plat: An LLM-Oriented Platform, Benchmark and Framework for Multi-UAV Collaborative Task Planning
MultiUAV-Plat supplies a new RESTful simulation platform and 1500-task benchmark where Agent4Drone reaches 57.9% task pass rate versus 30.6% for ReAct baseline across 75 multi-UAV missions.
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Co-GLANCE: Uncertainty-Aware Active Perception for Heterogeneous Robot Teaming
Co-GLANCE distills vision-language models into an end-to-end onboard model for occlusion segmentation and robot allocation, using conformal prediction plus selective abstention to trigger active perception and achieve 25-36% higher accuracy with 350x lower latency than cloud baselines.
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PerceptTwin: Semantic Scene Reconstruction for Iterative LLM Planning and Verification
PerceptTwin creates interactive simulations from open-vocabulary object maps for verifying and refining LLM robot plans, reporting ~39% higher success rates and up to 18% better human verification.
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Robust Instruction Compliance in Cooperative Multi-Agent Reinforcement Learning
MAVIC corrects Bellman backups at instruction boundaries by adjusting the incoming objective and restoring continuation value, enabling consistent estimation under stochastic instruction switching in cooperative MARL.
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Thought-Retriever: Don't Just Retrieve Raw Data, Retrieve Thoughts for Memory-Augmented Agentic Systems
Thought-Retriever retrieves filtered and organized thoughts from past queries to let LLMs handle arbitrarily long external knowledge without context-length limits.
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To Select or not to Select, that is the Question: Distilling Robot Skill Prediction into a Small Ensemble
A 133M-parameter ensemble of fine-tuned mpnet and MiniLM encoders achieves 83.5% accuracy on a 200-task synthetic benchmark for robot skill prediction, beating several larger zero-shot LLMs.
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Large Language Models for Multi-Robot Systems: A Survey
A survey that categorizes LLM uses in multi-robot systems across task allocation, motion planning, action generation, and human interaction, while noting challenges and future research opportunities.
- As You Wish: Mission Planning with Formal Verification using LLMs in Precision Agriculture