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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.05077 |
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| _version_ | 1866913978824785920 |
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| author | Abuelsamen, Luai Adebanjo, Temitope Lukman |
| author_facet | Abuelsamen, Luai Adebanjo, Temitope Lukman |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_05077 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Analyzing the Impact of Multimodal Perception on Sample Complexity and Optimization Landscapes in Imitation Learning Abuelsamen, Luai Adebanjo, Temitope Lukman Machine Learning Robotics 68T05, 62C10, 68T45 I.2.9; I.2.6; I.5.4 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. |
| title | Analyzing the Impact of Multimodal Perception on Sample Complexity and Optimization Landscapes in Imitation Learning |
| topic | Machine Learning Robotics 68T05, 62C10, 68T45 I.2.9; I.2.6; I.5.4 |
| url | https://arxiv.org/abs/2508.05077 |