Multimodal Anomaly Detection for Human-Robot Interaction
Ensuring safety and reliability in human-robot interaction (HRI) requires the timely detection of unexpected events that could lead to system failures or unsafe behaviours. Anomaly detection thus plays a critical role in enabling robots to recognize and respond to deviations from normal operation during collaborative tasks. While reconstruction models have been actively explored in HRI, approaches that operate directly on feature vectors remain largely unexplored. In this work, we propose MADRI, a framework that first transforms video streams into semantically meaningful feature vectors before performing reconstruction-based anomaly detection. Additionally, we augment these visual feature...
| Authors | Guilherme Ribeiro, Iordanis Antypas, Leonardo Bizzaro et al. |
|---|---|
| Primary category | cs.RO |
| Published | 10 April 2026 |
| Last updated at source | 10 April 2026 |
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