具現化エージェントは「猫型マグカップ」を見つけられるか？LLM誘導探索によるゼロショット物体ナビゲーション
Can an Embodied Agent Find Your "Cat-shaped Mug"? LLM-Guided Exploration for Zero-Shot Object Navigation
大規模言語モデルの常識推論を活用して未知環境で特定の物体を探索するゼロショット物体ナビゲーション手法LGXを提案し、RoboTHORで成功率を27%以上改善、実世界実験でも有効性を示した。
著者: Vishnu Sashank Dorbala, James F. Mullen, Dinesh Manocha
分類: cs.RO, cs.AI, cs.CL
原文アブストラクト
We present LGX (Language-guided Exploration), a novel algorithm for Language-Driven Zero-Shot Object Goal Navigation (L-ZSON), where an embodied agent navigates to a uniquely described target object in a previously unseen environment. Our approach makes use of Large Language Models (LLMs) for this task by leveraging the LLM's commonsense reasoning capabilities for making sequential navigational decisions. Simultaneously, we perform generalized target object detection using a pre-trained Vision-Language grounding model. We achieve state-of-the-art zero-shot object navigation results on RoboTHOR with a success rate (SR) improvement of over 27% over the current baseline of the OWL-ViT CLIP on Wheels (OWL CoW). Furthermore, we study the usage of LLMs for robot navigation and present an analysis of various prompting strategies affecting the model output. Finally, we showcase the benefits of our approach via \textit{real-world} experiments that indicate the superior performance of LGX in detecting and navigating to visually unique objects.
