Vision-Based Safe Human-Robot Collaboration with Uncertainty Guarantees

arXiv:2604.15221v1 Announce Type: cross Abstract: We propose a framework for vision-based human pose estimation and motion prediction that gives conformal prediction guarantees for certifiably safe human-robot collaboration. Our framework combines aleatoric uncertainty estimation with OOD detection for high probabilistic confidence. To integrate our pipeline in certifiable safety frameworks, we propose conformal prediction sets for human motion predictions with high, valid confidence. We evaluate our pipeline on recorded human motion data and a real-world human-robot collaboration setting.

Related

• Safe Human-to-Humanoid Motion Imitation Using Control Barrier Functions
• Multimodal Anomaly Detection for Human-Robot Interaction
• EgoVerse: An Egocentric Human Dataset for Robot Learning from Around the World
• LIDEA: Human-to-Robot Imitation Learning via Implicit Feature Distillation and Explicit Geometry Alignment

Source: arXiv cs.CV | 2026-04-17