A framework coupling LLM direction proposals with an external evaluator and a Gaussian-mixture experience memory reduces object-search path length and time versus heuristic baselines in real-world large-scale robot trials.
Reinforcement learning for mobile robotics exploration: A survey.IEEE Transactions on Neural Networks and Learning Systems, 34(8):3796–3810, 2021
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.RO 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
GET: Goal-directed Exploration and Targeting for Large-Scale Unknown Environments
A framework coupling LLM direction proposals with an external evaluator and a Gaussian-mixture experience memory reduces object-search path length and time versus heuristic baselines in real-world large-scale robot trials.