Analyzing the Impact of Multimodal Perception on Sample Complexity and Optimization Landscapes in Imitation Learning
This work addresses theoretical foundations for multimodal imitation learning, providing insights into why methods like PerAct and CLIPort perform better, but it is incremental as it builds on existing learning theory.
The paper analyzes how multimodal perception affects sample complexity and optimization landscapes in imitation learning, showing that integrated multimodal policies achieve tighter generalization bounds and more favorable landscapes than unimodal ones.
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.