Targeted Modality Dropout for Real-Robot Manipulation Robust to Intermittent Vision Loss

arXiv:2610.09566 · cs.RO · Submitted 2026-10-07 · Read on arXiv

cs.RO

Submitted: 2026-10-07

Updated: 2026-10-07

Comments: 7 pages, 5 figures, 2 tables. Submitted to the 2027 IEEE/SICE International Symposium on System Integration (SII 2027)

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Regularization — 20× in this paper · explained in 3 episodes
• Robustness — 12× in this paper · explained in 5 episodes
• Transformer — 9× in this paper · explained in 2 episodes
• Targeted Modality Dropout — 7× in this paper
• Proceedings of the Conference on Robot Learning — 3× in this paper
• Action Decoder — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• International Conference on Learning Representations — 2× in this paper
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Flow Matching — 1× in this paper · explained in 8 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Random Modality Dropout — 1× in this paper
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Reward Function — 1× in this paper · explained in 2 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes

Sources

• Compact Task-Aligned Imitation Learning for Laboratory Automation
• GelFusion: Enhancing Robotic Manipulation under Visual Constraints via Visuotactile Fusion
• Reinforcement Learning-Guided Retrieval with Soft Fusion for Robust Multimodal Imitation Learning under Missing Modalities
• DINOv3
• SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

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