多模态感知可降低模仿学习样本需求并改善优化效果。
Analyzing the Impact of Multimodal Perception on Sample Complexity and Optimization Landscapes in Imitation Learning
- 融合视觉、本体感觉与语言的多模态策略提升泛化能力。
- 理论证明多模态模型比单模态更优，优化空间更平滑。
- 适合关注模仿学习理论基础的研究者与工程师。
本文从统计学习理论角度分析多模态模仿学习的理论基础。研究探讨了多模态感知（如RGB-D、本体感觉、语言）如何影响模仿策略的样本复杂度与优化景观。基于多模态学习理论的最新进展，我们证明：合理整合的多模态策略可获得更紧的泛化界，并具备更优的优化特性，优于单模态方法。论文系统回顾了解释PerAct、CLIPort等架构性能优越性的理论框架，将这些实证结果与Rademacher复杂度、PAC学习及信息论等基本概念相联系。
原文摘要 · Abstract (English)
This paper examines the theoretical foundations of multimodal imitation learning through the lens of statistical learning theory. We analyze how multimodal perception (RGB-D, proprioception, language) affects sample complexity and optimization landscapes in imitation policies. Building on recent advances in multimodal learning theory, we show that properly integrated multimodal policies can achieve tighter generalization bounds and more favorable optimization landscapes than their unimodal counterparts. We provide a comprehensive review of theoretical frameworks that explain why multimodal architectures like PerAct and CLIPort achieve superior performance, connecting these empirical results to fundamental concepts in Rademacher complexity, PAC learning, and information theory.
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