身体性マルチモーダル大規模モデルの探求：開発・データセット・今後の展望
Exploring Embodied Multimodal Large Models: Development, Datasets, and Future Directions
知覚・認知・行動を統合する身体性マルチモーダル大規模モデル（EMLM）について、LLMや大規模視覚モデルを含む発展、知覚・ナビゲーション・相互作用・シミュレーションの各側面、学習・評価用データセット、課題と今後の方向性を包括的にレビューした論文。
著者: Shoubin Chen, Zehao Wu, Kai Zhang, Chunyu Li, Baiyang Zhang, Fei Ma, Fei Richard Yu, Qingquan Li
分類: cs.RO, cs.AI
原文アブストラクト
Embodied multimodal large models (EMLMs) have gained significant attention in recent years due to their potential to bridge the gap between perception, cognition, and action in complex, real-world environments. This comprehensive review explores the development of such models, including Large Language Models (LLMs), Large Vision Models (LVMs), and other models, while also examining other emerging architectures. We discuss the evolution of EMLMs, with a focus on embodied perception, navigation, interaction, and simulation. Furthermore, the review provides a detailed analysis of the datasets used for training and evaluating these models, highlighting the importance of diverse, high-quality data for effective learning. The paper also identifies key challenges faced by EMLMs, including issues of scalability, generalization, and real-time decision-making. Finally, we outline future directions, emphasizing the integration of multimodal sensing, reasoning, and action to advance the development of increasingly autonomous systems. By providing an in-depth analysis of state-of-the-art methods and identifying critical gaps, this paper aims to inspire future advancements in EMLMs and their applications across diverse domains.
