# Uncertainty Aware-Predictive Control Barrier Functions: Safer Human Robot Interaction through Probabilistic Motion Forecasting

arXiv: https://arxiv.org/abs/2508.20812 · HTML: https://mlsanity.com/paper/2508.20812/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 46 references that are in the map.
- First posted on arXiv 28 Aug 2025.

## Builds on (most cited first, 27 of 27)

- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [Control Barrier Functions: Theory and Applications](https://mlsanity.com/paper/1903.11199/) (2019)
- [Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction](https://mlsanity.com/paper/2002.11927/) (2020)
- [Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2005.08514/) (2020)
- [Transformer Networks for Trajectory Forecasting](https://mlsanity.com/paper/2003.08111/) (2020)
- [Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion Prediction](https://mlsanity.com/paper/2003.08802/) (2020)
- [Back to MLP: A Simple Baseline for Human Motion Prediction](https://mlsanity.com/paper/2207.01567/) (2022)
- [Remember Intentions: Retrospective-Memory-based Trajectory Prediction](https://mlsanity.com/paper/2203.11474/) (2022)
- [Uncertainty-aware State Space Transformer for Egocentric 3D Hand Trajectory Forecasting](https://mlsanity.com/paper/2307.08243/) (2023)
- [Pose Forecasting in Industrial Human-Robot Collaboration](https://mlsanity.com/paper/2208.07308/) (2022)
- [Robotic Vision for Human-Robot Interaction and Collaboration: A Survey and Systematic Review](https://mlsanity.com/paper/2307.15363/) (2023)
- [Action Anticipation: Reading the Intentions of Humans and Robots](https://mlsanity.com/paper/1802.02788/) (2018)
- [Predictive control barrier functions: Enhanced safety mechanisms for learning-based control](https://mlsanity.com/paper/2105.10241/) (2021)
- [Backup Control Barrier Functions: Formulation and Comparative Study](https://mlsanity.com/paper/2104.11332/) (2021)
- [Comparative Analysis of Control Barrier Functions and Artificial Potential Fields for Obstacle Avoidance](https://mlsanity.com/paper/2010.09819/) (2020)
- [Rule-Based Safety-Critical Control Design using Control Barrier Functions with Application to Autonomous Lane Change](https://mlsanity.com/paper/2103.12382/) (2021)
- [Robust Control Barrier Functions under High Relative Degree and Input Constraints for Satellite Trajectories](https://mlsanity.com/paper/2107.04094/) (2021)
- [Approximate Optimal Control for Safety-Critical Systems with Control Barrier Functions](https://mlsanity.com/paper/2008.04122/) (2020)
- [Exploring 3D Human Pose Estimation and Forecasting from the Robot's Perspective: The HARPER Dataset](https://mlsanity.com/paper/2403.14447/) (2024)
- [Predictive Control Barrier Functions for Online Safety Critical Control](https://mlsanity.com/paper/2204.00208/) (2022)
- [OO-dMVMT: A Deep Multi-view Multi-task Classification Framework for Real-time 3D Hand Gesture Classification and Segmentation](https://mlsanity.com/paper/2304.05956/) (2023)
- [Probabilistically safe controllers based on control barrier functions and scenario model predictive control](https://mlsanity.com/paper/2409.06834/) (2024)
- [Gaussian Control Barrier Functions : A Non-Parametric Paradigm to Safety](https://mlsanity.com/paper/2203.15474/) (2022)
- [Future-Focused Control Barrier Functions for Autonomous Vehicle Control](https://mlsanity.com/paper/2204.00127/) (2022)
- [Safe Control Synthesis Using Environmentally Robust Control Barrier Functions](https://mlsanity.com/paper/2304.11600/) (2023)
- [Observer-Based Environment Robust Control Barrier Functions for Safety-critical Control with Dynamic Obstacles](https://mlsanity.com/paper/2403.13288/) (2024)

## Abstract (arXiv)

To enable flexible, high-throughput automation in settings where people and robots share workspaces, collaborative robotic cells must reconcile stringent safety guarantees with the need for responsive and effective behavior. A dynamic obstacle is the stochastic, task-dependent variability of human motion: when robots fall back on purely reactive or worst-case envelopes, they brake unnecessarily, stall task progress, and tamper with the fluidity that true Human-Robot Interaction demands. In recent years, learning-based human-motion prediction has rapidly advanced, although most approaches produce worst-case scenario forecasts that often do not treat prediction uncertainty in a well-structured way, resulting in over-conservative planning algorithms, limiting their flexibility. We introduce Uncertainty-Aware Predictive Control Barrier Functions (UA-PCBFs), a unified framework that fuses probabilistic human hand motion forecasting with the formal safety guarantees of Control Barrier Functions. In contrast to other variants, our framework allows for dynamic adjustment of the safety margin thanks to the human motion uncertainty estimation provided by a forecasting module. Thanks to uncertainty estimation, UA-PCBFs empower collaborative robots with a deeper understanding of future human states, facilitating more fluid and intelligent interactions through informed motion planning. We validate UA-PCBFs through comprehensive real-world experiments with an increasing level of realism, including automated setups (to perform exactly repeatable motions) with a robotic hand and direct human-robot interactions (to validate promptness, usability, and human confidence). Relative to state-of-the-art HRI architectures, UA-PCBFs show better performance in task-critical metrics, significantly reducing the number of violations of the robot's safe space during interaction with respect to the state-of-the-art.