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Investigating the Role of Instruction Variety and Task Difficulty in Robotic Manipulation Tasks

EMNLP 2024(2024)

Cited 0|Views14
Abstract
Evaluating the generalisation capabilities of multimodal models based solely on their performance on out-of-distribution data fails to capture their true robustness. This work introduces a comprehensive evaluation framework that systematically examines the role of instructions and inputs in the generalisation abilities of such models, considering architectural design, input perturbations across language and vision modalities, and increased task complexity. The proposed framework uncovers the resilience of multimodal models to extreme instruction perturbations and their vulnerability to observational changes, raising concerns about overfitting to spurious correlations. By employing this evaluation framework on current Transformer-based multimodal models for robotic manipulation tasks, we uncover limitations and suggest future advancements should focus on architectural and training innovations that better integrate multimodal inputs, enhancing a model's generalisation prowess by prioritising sensitivity to input content over incidental correlations.
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要点】:本文提出了一种全面评估框架,用以研究指令多样性和任务难度在机器人操作任务中 multimodal 模型的泛化能力,发现现有模型在极端指令扰动下的鲁棒性以及对于观测变化的脆弱性,指出模型过度拟合于偶然相关性,并建议未来的研究应着重于提升模型对输入内容的敏感性。

方法】:作者通过考虑架构设计、跨语言和视觉模态的输入扰动以及增加任务复杂性,系统性地评估了指令和输入在模型泛化能力中的作用。

实验】:研究者在当前的基于 Transformer 的用于机器人操作任务的 multimodal 模型上应用了该评估框架,发现了模型的局限性,并指出未来应关注能够更好地整合多模态输入的架构和训练创新。具体使用的数据集名称和详细实验结果未在摘要中提供。