Vision-Based Safe Human-Robot Collaboration with Uncertainty Guarantees
Provides uncertainty guarantees for safe human-robot interaction, addressing a key bottleneck in certifiable safety for collaborative robots.
A framework for vision-based human pose estimation and motion prediction with conformal prediction guarantees enables certifiably safe human-robot collaboration, validated on real-world data.
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.