Analyzing the Impact of Multimodal Perception on Sample Complexity and Optimization Landscapes in Imitation Learning

TL;DR

This paper provides a theoretical analysis of how multimodal perception improves sample efficiency and optimization in imitation learning, supported by frameworks like Rademacher complexity and PAC learning.

Contribution

It offers a theoretical foundation explaining why multimodal architectures outperform unimodal ones in imitation learning tasks.

Findings

Multimodal policies have tighter generalization bounds.

Multimodal architectures exhibit more favorable optimization landscapes.

Theoretical frameworks connect multimodal learning to fundamental statistical learning concepts.

Abstract

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.