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Language-Conditioned Offline RL for Multi-Robot Navigation
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We present a method for developing navigation policies for multi-robot teams that interpret and follow natural language instructions. We condition these policies on embeddings from pretrained Large Language Models (LLMs), and train them via offline reinforcement learning with as little as 20 minutes of randomly-collected data. Experiments on a team of five real robots show that these policies generalize well to unseen commands, indicating an understanding of the LLM latent space. Our method requires no simulators or environment models, and produces low-latency control policies that can be deployed directly to real robots without finetuning. We provide videos of our experiments at https://sites.google.com/view/llm-marl.
Forward citations
Cited by 3 Pith papers
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Prompting Robot Teams with Natural Language
A natural-language team command is distilled into a small recurrent network that encodes the task as an automaton, while a graph-neural-network policy executes it in a decentralized, real-time manner.
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Can Pretrained Vision-Language Embeddings Alone Guide Robot Navigation?
A behavior-cloning policy trained only on frozen SigLIP embeddings reaches 74% of language-specified targets in a simple simulator, versus 100% for a state-aware expert, and takes 3.2x more steps.
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A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data
A metric-oriented survey that classifies intrinsic quality and trustworthiness metrics for LLM-generated data across six modalities and documents systematic evaluation gaps in the current literature.
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