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

• URL: http://arxiv.org/abs/2508.20812v1
• Date: Thu, 28 Aug 2025 14:11:26 GMT
• Title: Uncertainty Aware-Predictive Control Barrier Functions: Safer Human Robot Interaction through Probabilistic Motion Forecasting
• Authors: Lorenzo Busellato, Federico Cunico, Diego Dall'Alba, Marco Emporio, Andrea Giachetti, Riccardo Muradore, Marco Cristani,
• Abstract summary: Uncertainty-Aware Predictive Control Barrier Functions fuses probabilistic human hand motion forecasting with the formal safety guarantees of Control Barrier Functions.<n> UA-PCBFs empower collaborative robots with a deeper understanding of future human states.<n>Relative to state-of-the-art HRI architectures, UA-PCBFs show better performance in task-critical metrics.
• Score: 13.020006323600251
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• BarrierSteer: LLM Safety via Learning Barrier Steering [83.12893815611052]
  BarrierSteer is a novel framework that formalizes safety by embedding learned non-linear safety constraints directly into the model's latent representation space.<n>We show that BarrierSteer substantially reduces adversarial success rates, decreases unsafe generations, and outperforms existing methods.
  arXiv  Detail & Related papers  (2026-02-23T18:19:46Z)
• HHI-Assist: A Dataset and Benchmark of Human-Human Interaction in Physical Assistance Scenario [63.77482302352545]
  HHI-Assist is a dataset comprising motion capture clips of human-human interactions in assistive tasks.<n>Our work has the potential to significantly enhance robotic assistance policies.
  arXiv  Detail & Related papers  (2025-09-12T09:38:17Z)
• Towards Immersive Human-X Interaction: A Real-Time Framework for Physically Plausible Motion Synthesis [51.95817740348585]
  Human-X is a novel framework designed to enable immersive and physically plausible human interactions across diverse entities.<n>Our method jointly predicts actions and reactions in real-time using an auto-regressive reaction diffusion planner.<n>Our framework is validated in real-world applications, including virtual reality interface for human-robot interaction.
  arXiv  Detail & Related papers  (2025-08-04T06:35:48Z)
• Designing Control Barrier Function via Probabilistic Enumeration for Safe Reinforcement Learning Navigation [55.02966123945644]
  We propose a hierarchical control framework leveraging neural network verification techniques to design control barrier functions (CBFs) and policy correction mechanisms.<n>Our approach relies on probabilistic enumeration to identify unsafe regions of operation, which are then used to construct a safe CBF-based control layer.<n>These experiments demonstrate the ability of the proposed solution to correct unsafe actions while preserving efficient navigation behavior.
  arXiv  Detail & Related papers  (2025-04-30T13:47:25Z)
• Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics [68.36528819227641]
  This paper systematically evaluates the robustness of Vision-Language-Action (VLA) models.<n>We introduce two untargeted attack objectives that leverage spatial foundations to destabilize robotic actions, and a targeted attack objective that manipulates the robotic trajectory.<n>We design an adversarial patch generation approach that places a small, colorful patch within the camera's view, effectively executing the attack in both digital and physical environments.
  arXiv  Detail & Related papers  (2024-11-18T01:52:20Z)
• Deception Game: Closing the Safety-Learning Loop in Interactive Robot Autonomy [7.915956857741506]
  Existing safety methods often neglect the robot's ability to learn and adapt at runtime, leading to overly conservative behavior. This paper proposes a new closed-loop paradigm for synthesizing safe control policies that explicitly account for the robot's evolving uncertainty.
  arXiv  Detail & Related papers  (2023-09-03T20:34:01Z)
• Active Uncertainty Reduction for Safe and Efficient Interaction Planning: A Shielding-Aware Dual Control Approach [9.07774184840379]
  We present a novel algorithmic approach to enable active uncertainty reduction for interactive motion planning based on the implicit dual control paradigm. Our approach relies on sampling-based approximation of dynamic programming, leading to a model predictive control problem that can be readily solved by real-time gradient-based optimization methods.
  arXiv  Detail & Related papers  (2023-02-01T01:34:48Z)
• SHARP: Shielding-Aware Robust Planning for Safe and Efficient Human-Robot Interaction [5.804727815849655]
  " Shielding" control scheme overrides the robot's nominal plan with a safety fallback strategy when a safety-critical event is imminent. We propose a new shielding-based planning approach that allows the robot to plan efficiently by explicitly accounting for possible future shielding events.
  arXiv  Detail & Related papers  (2021-10-02T17:01:59Z)
• SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots [112.2491765424719]
  We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal. We use model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints. recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution. A Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal.
  arXiv  Detail & Related papers  (2021-08-03T02:56:21Z)
• Fast-reactive probabilistic motion planning for high-dimensional robots [15.082715993594121]
  p-Chekov is a fast-reactive motion planning system that can provide safety guarantees for high-dimensional robots suffering from process noises and observation noises. Comprehensive theoretical and empirical analysis shows that p-Chekov can effectively satisfy user-specified chance constraints over collision risk in practical robotic manipulation tasks.
  arXiv  Detail & Related papers  (2020-12-03T17:51:07Z)
• Risk-Sensitive Sequential Action Control with Multi-Modal Human Trajectory Forecasting for Safe Crowd-Robot Interaction [55.569050872780224]
  We present an online framework for safe crowd-robot interaction based on risk-sensitive optimal control, wherein the risk is modeled by the entropic risk measure. Our modular approach decouples the crowd-robot interaction into learning-based prediction and model-based control. A simulation study and a real-world experiment show that the proposed framework can accomplish safe and efficient navigation while avoiding collisions with more than 50 humans in the scene.
  arXiv  Detail & Related papers  (2020-09-12T02:02:52Z)
• Safe Multi-Agent Interaction through Robust Control Barrier Functions with Learned Uncertainties [36.587645093055926]
  Multi-Agent Control Barrier Functions (CBF) have emerged as a computationally efficient tool to guarantee safety in multi-agent environments. This work aims to learn high-confidence bounds for these dynamic uncertainties using Matrix-Variate Gaussian Process models. We transform the resulting min-max robust CBF into a quadratic program, which can be efficiently solved in real time.
  arXiv  Detail & Related papers  (2020-04-11T00:56:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.