大規模言語モデルを用いた部分観測ロボティクスタスクの対話型プランニング
Interactive Planning Using Large Language Models for Partially Observable Robotics Tasks
部分観測環境でロボットが不足情報を収集しながら状態を推定し、LLMが対話的に行動を計画する手法を提案。GPT-4とファインチューニングしたLlama 2を比較し、シミュレーションと実環境で検証した。
著者: Lingfeng Sun, Devesh K. Jha, Chiori Hori, Siddarth Jain, Radu Corcodel, Xinghao Zhu, Masayoshi Tomizuka, Diego Romeres
分類: cs.RO, cs.AI
原文アブストラクト
Designing robotic agents to perform open vocabulary tasks has been the long-standing goal in robotics and AI. Recently, Large Language Models (LLMs) have achieved impressive results in creating robotic agents for performing open vocabulary tasks. However, planning for these tasks in the presence of uncertainties is challenging as it requires \enquote{chain-of-thought} reasoning, aggregating information from the environment, updating state estimates, and generating actions based on the updated state estimates. In this paper, we present an interactive planning technique for partially observable tasks using LLMs. In the proposed method, an LLM is used to collect missing information from the environment using a robot and infer the state of the underlying problem from collected observations while guiding the robot to perform the required actions. We also use a fine-tuned Llama 2 model via self-instruct and compare its performance against a pre-trained LLM like GPT-4. Results are demonstrated on several tasks in simulation as well as real-world environments. A video describing our work along with some results could be found here.
