加州大学最新！做什么？教VLA模型拒绝不可能的任务

Core Viewpoint - The article discusses the development and performance of the VLA model in handling robotic tasks, particularly focusing on its ability to detect and respond to false premise instructions through the proposed IVA framework, which enhances the model's robustness in real-world applications [4][10]. Group 1: Problem Identification and Solution - The VLA model excels in various robotic tasks by relying on multimodal inputs, but it struggles with false premise instructions, which involve commands that reference non-existent objects or conditions [6][10]. - The IVA framework is introduced to address this issue, enabling the model to detect unexecutable commands, clarify or correct them through language, and associate reasonable alternatives with perception and action [4][10]. Group 2: Research Gaps and Contributions - Current research primarily focuses on successful execution rates of correct commands, neglecting the handling of ambiguous or unexecutable instructions [6][10]. - The core contributions of this work include the introduction of the IVA framework, the construction of a large-scale dataset for training, and validation of the model's performance across eight robotic tasks, demonstrating significant improvements in detecting false premises and executing valid commands [10][25]. Group 3: Experimental Results - The IVA framework achieved a false premise detection accuracy of 97.56% and a 50.78% increase in successful responses under false premise scenarios compared to baseline models [5][25]. - In various tasks, IVA outperformed the LLARVA model in overall success rates and false premise detection rates, with only minor reductions in success rates for real premise commands [25][28]. Group 4: Limitations and Future Directions - The dataset used for training is limited to a simulated environment, which may not fully represent real-world human-robot interactions, and the distribution of false premises may not align with actual occurrences [26][27]. - The IVA framework currently lacks the ability to handle complex, multi-turn clarifications and may struggle with longer, more ambiguous human instructions [26][27].