REDACT: Robust Perceptive Locomotion under Unseen Visual Corruption

2026-09-21

AI Summary

本文提出REDACT框架，通过改进视觉编码器、持续特征掩码和新的共识门控算法，在未见过的视觉损坏下保持有效的深度信息，提高机器人在未知环境中的移动成功率。

Abstract

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.

Problem

• depth-conditioned locomotion
• visual corruption
• unseen corruption
• sensor noise
• occlusion

Innovation

• Depth-conditioned Locomotion
• Consensus-Gating Algorithm
• Persistent Feature Masking
• Approximate Conformal Calibration
• Zero-Shot Transfer