用专家混合框架同时检测机器人动作与环境异常，提速60%。
Multimodal Anomaly Detection with a Mixture-of-Experts
- 分两路：视觉语言模型看环境，高斯混合回归追踪力与运动偏差
- 动态融合机制根据置信度选最优检测器，延迟降低60%
- 适合家庭与工业场景的机器人故障早发现，提升系统可靠性
随着机器人在各类场景中部署增多，鲁棒的多模态异常检测愈发重要。机器人操作中的故障通常源于两类：一是因任务模型不足或硬件限制导致的机器人自身异常；二是由环境动态变化或外部干扰引起的环境异常。传统方法分别采用低层统计建模处理前者，或依赖深度学习视觉感知处理后者，二者计算需求和训练数据要求不同。为有效捕捉两类异常，本文提出一种混合专家框架，整合视觉-语言模型用于环境监控，以及基于高斯混合回归的检测器以追踪交互力与机器人运动的偏差。引入基于置信度的融合机制，动态选择最可靠的检测器。在两个机器人系统上评估了家庭与工业任务，相比单一检测器，检测延迟减少60%，帧级异常检测性能显著提升。
原文摘要 · Abstract (English)
With a growing number of robots being deployed across diverse applications, robust multimodal anomaly detection becomes increasingly important. In robotic manipulation, failures typically arise from (1) robot-driven anomalies due to an insufficient task model or hardware limitations, and (2) environment-driven anomalies caused by dynamic environmental changes or external interferences. Conventional anomaly detection methods focus either on the first by low-level statistical modeling of proprioceptive signals or the second by deep learning-based visual environment observation, each with different computational and training data requirements. To effectively capture anomalies from both sources, we propose a mixture-of-experts framework that integrates the complementary detection mechanisms with a visual-language model for environment monitoring and a Gaussian-mixture regression-based detector for tracking deviations in interaction forces and robot motions. We introduce a confidence-based fusion mechanism that dynamically selects the most reliable detector for each situation. We evaluate our approach on both household and industrial tasks using two robotic systems, demonstrating a 60% reduction in detection delay while improving frame-wise anomaly detection performance compared to individual detectors.
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