自然言語指示に従うマルチロボットナビゲーションのためのオフライン強化学習
Language-Conditioned Offline RL for Multi-Robot Navigation
LLMの埋め込みを条件としたナビゲーション方策を、20分程度のランダム収集データによるオフライン強化学習で訓練し、5台の実ロボットで未見の指示にも汎化することを示した。
著者: Steven Morad, Ajay Shankar, Jan Blumenkamp, Amanda Prorok
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
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.
