想清楚再动手：具身智能也要学会脑补未来和择优执行

Core Viewpoint - The article discusses the development of a new framework called FOREWARN, which combines world models and multimodal language reasoning to enhance the deployment intelligence of robotic systems, enabling them to make real-time decisions without additional data collection [5][21]. Group 1: Research Background - The first author, Wu Yilin, is a second-year PhD student at Carnegie Mellon University, focusing on object manipulation and lifelong learning in robotics [1]. - The second author, Tian Ran, is a PhD candidate at UC Berkeley and a research scientist at NVIDIA, working on the safe and reliable application of foundational models in robotics [2]. Group 2: Challenges in Deployment Intelligence - Current embodied intelligence models often struggle in real-world deployments due to their inability to adapt to environmental disturbances and user preference variations, leading to execution failures [3][21]. - The two main challenges in deployment are predicting the future consequences of actions and evaluating the predicted outcomes against task goals and user preferences [8][10]. Group 3: FOREWARN Framework - The FOREWARN framework consists of two modules: Foresight (simulating future outcomes) and Forethought (evaluating those outcomes), allowing for a more structured decision-making process [11]. - The system uses a world model to predict environmental changes based on candidate actions and employs a fine-tuned multimodal language model to interpret these predictions semantically [12][18]. Group 4: Innovation Highlights - The framework achieves cross-modal alignment between the world model's predictions and the language model's understanding, facilitating a closed-loop reasoning process from perception to decision-making [18]. - FOREWARN automates the decision-making process, significantly reducing deployment barriers and labor costs by enabling real-time selection of optimal action plans [19]. Group 5: Performance Evaluation - The introduction of the FOREWARN framework improved the success rate of robotic tasks from below 30% to 70%-80%, demonstrating its effectiveness in adapting to changing task instructions and user preferences [21]. - Even under varying conditions, the system maintained a success rate of 60%-80%, showcasing its robustness and adaptability [21]. Group 6: Future Directions - The research team identifies three challenges for broader application: enhancing the diversity and generalization of underlying strategies, addressing data scarcity issues, and optimizing reasoning efficiency and computational costs [23]. - The ongoing advancements in multimodal language models and world models are expected to further enhance the deployment intelligence of robots, enabling them to autonomously select safe and reasonable operational plans based on natural language instructions [23].