REDACT：未知视觉损坏下的鲁棒感知运动
REDACT: Robust Perceptive Locomotion under Unseen Visual Corruption
- University of Southampton（南安普顿大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
REDACT提出教师-学生框架，结合改进视觉编码器、持久特征掩蔽和共识门控算法，在未知视觉损坏下保留深度信息，提升穿越成功率并实现真实环境零样本迁移。
AI中文摘要：
基于深度条件的运动策略已展现出令人印象深刻的敏捷动作，但当观测超出其训练分布时，可能被引导至不可预测的行为。遮挡、无效返回、传感器噪声和视觉干扰物会使部署观测偏离名义模拟深度。虽然合成传感器增强针对特定退化，但其本身并未定义在训练中遗漏的损坏类别下的行为。为解决训练时覆盖的不足，我们提出REDACT（Retaining Evidence Despite Artifacts for Continued Traversal），一种教师-学生框架，结合改进的视觉编码器架构、持久特征掩蔽和一种新颖的共识门控算法，以在未建模损坏下保留有用的深度信息。该门控仅基于干净观测使用近似共形校准，无需事先了解损坏类型。在干净模拟深度上训练后，REDACT在未知损坏下保留有用的视觉信息，支持比现有跑酷基线更高的穿越成功率。跨损坏类别的深度增强评估进一步表明，REDACT在增强覆盖缺失处提升了鲁棒性。真实世界试验展示了向具有陌生场景内容的结构化和森林环境的零样本迁移。
英文摘要：
Depth-conditioned locomotion policies have demonstrated impressive agile maneuvers, but can be steered to unpredictable actions when observations are outside their training distribution. Occlusion, invalid returns, sensor noise, and visual distractors can shift deployment observations away from nominal simulated depth. While synthetic sensor augmentation targets specified degradations, it does not by itself define behavior under corruption families omitted from training. To address gaps in training-time coverage, we present REDACT (Retaining Evidence Despite Artifacts for Continued Traversal), a teacher-student framework combining an improved visual encoder architecture, persistent feature masking, and a novel consensus-gating algorithm to retain useful depth information under unmodeled corruption. The gate uses approximate conformal calibration on clean observations alone, requiring no prior knowledge of the corruption type. Trained on clean simulated depth, REDACT retains useful visual information under unseen corruption, supporting higher traversal success than existing parkour baselines. Evaluation of depth augmentation across corruption families further shows that REDACT improves robustness where augmentation coverage is missing. Real-world trials demonstrate zero-shot transfer to structured and forested environments with unfamiliar scene content.
