Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.09566

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

Genki Shikada·Kazuki Osamura·Masaru Ide·Tetsuya Ogata·Kanata Suzuki
View original source ↗Read source document ↗Export metadata ↓

Abstract

Imitation learning policies that integrate multiple sensory modalities are prone to overreliance on a dominant modality, such as vision, during training, which can disrupt policy execution when that modality is lost at inference time. In this paper, we introduce Targeted Modality Dropout (TMD), in which the dependence on each modality is estimated using attention and the most dominant modality is selectively dropped. This is combined with entropy regularization over the dependence distribution. Through real-robot evaluation using a bimanual manipulator, we show that under vision loss the success rate of the baseline policy drops substantially, whereas TMD sustains task execution. In contrast, a conventional dropout that selects the dropped modality at random, without the entropy regularization, fails on many tasks even without vision loss.

Explore related subjects

cs.RO

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Genki Shikada, Kazuki Osamura, Masaru Ide, Tetsuya Ogata, Kanata Suzuki. 2026-10-07. Targeted Modality Dropout for Real-Robot Manipulation Robust to Intermittent Vision Loss. https://arxiv.org/abs/2610.09566

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

The Kinetics Observer: A Tightly Coupled Estimator for Legged Robots

This paper presents the Kinetics Observer, a novel proprioceptive state estimator for legged robots designed to provide the feedback required for versatile locomotion and physical interaction with the environment, with enhanced robustness to contact slippage. Its core contribution is a tight coupling between whole-body kinematics and external wrenches through a contact dynamics model, enabling the consistent fusion of leg kinematics, IMU, and wrench sensor measurements for real-time joint estimation of centroidal kinematics, contact rest poses, contact wrenches, and disturbance wrenches. By exploiting contact wrench measurements as correction terms, the proposed observer increases sensing redundancy, which provides observability of contact slippage relative to the centroid frame and enhanced robustness to modeling and sensor errors. The approach is experimentally evaluated on two humanoid robots across three scenarios totaling thirteen walking sequences, including long-distance walking with repeated contact changes, locomotion over slippery obstacles, and non-coplanar multicontact motion.

cs.RO↗
arXiv2026

RoboAug: One Annotation to Hundreds of Scenes via Region-Contrastive Data Augmentation for Robotic Manipulation

Enhancing the generalization of robotic learning in diverse unseen environments remains a fundamental challenge. Existing approaches often rely on large-scale pretraining, which is labor-intensive and time-consuming, or semantic data augmentation methods that assume flawless upstream object detection in real-world scenarios. In this work, we propose RoboAug, a novel generative data augmentation framework that reduces reliance on large-scale pretraining and perfect visual recognition by requiring only a single image with bounding box annotations for dataset construction. Leveraging this minimal supervision, RoboAug employs pretrained generative models for precise semantic augmentation and introduces a plug-and-play region-contrastive loss to guide attention toward task-relevant regions, thereby enhancing generalization and task success rates. Extensive real-world experiments on UR-5e, AgileX, and Tian Gong 2.0 demonstrate that RoboAug consistently outperforms state-of-the-art augmentation baselines under background, distractor, and lighting shifts. Our project is available at https://x-roboaug.github.io/.

cs.RO↗
arXiv2026

ActionCodec: What Makes for Good Action Tokenizers

Vision-Language-Action (VLA) models leveraging the native autoregressive paradigm of Vision-Language Models (VLMs) have demonstrated superior instruction-following and training efficiency. Central to this paradigm is action tokenization, yet its design has primarily focused on reconstruction fidelity, failing to address its direct impact on VLA optimization. Consequently, the fundamental question of \textit{what makes for good action tokenizers} remains unanswered. In this paper, we bridge this gap by establishing design principles specifically from the perspective of VLA optimization. We identify a set of best practices based on information-theoretic insights, including maximized temporal token overlap, minimized vocabulary redundancy, enhanced multimodal mutual information, and token independence. Guided by these principles, we introduce \textbf{ActionCodec}, a high-performance action tokenizer that significantly enhances both training efficiency and VLA performance across diverse simulation and real-world benchmarks. Notably, on LIBERO, a SmolVLM2-2.2B fine-tuned with ActionCodec achieves a 95.5\% success rate without any robotics pre-training. With advanced architectural enhancements, this reaches 97.4\%, representing a new SOTA for VLA models without robotics pre-training. We believe our established design principles, alongside the released model, will provide a clear roadmap for the community to develop more effective action tokenizers.

cs.RO↗