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

2025-08-28

AI Summary

In human-robot shared environments, the inherent randomness of human motion and dynamic task requirements pose a fundamental safety-efficiency trade-off; existing prediction methods often yield worst-case estimates while neglecting predictive uncertainty, leading to overly conservative safety control. Method: We propose Uncertainty-Aware Prediction-Control Barrier Functions (UA-PCBFs), the first framework to explicitly embed quantified uncertainty from probabilistic human motion prediction into control barrier functions, enabling dynamic, adaptive safety margin adjustment. Our approach integrates deep learning–based trajectory forecasting with real-time optimization-based control, forming an end-to-end uncertainty-aware planning architecture. Contribution/Results: Experiments demonstrate that UA-PCBFs reduce safety constraint violations by 62% and decrease task completion time by 28% compared to baseline methods, while significantly improving interaction fluency and operator trust.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: Human-Robot Interaction
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

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.

Problem

• Ensuring robot safety while maintaining flexible human-robot interaction workflows
• Addressing over-conservative planning due to uncertain human motion predictions
• Integrating probabilistic motion forecasting with formal safety guarantees

Innovation

• Unified framework combining probabilistic motion forecasting with safety guarantees
• Dynamic safety margin adjustment based on uncertainty estimation
• Enables fluid human-robot interaction through informed motion planning