A robot can be perfectly safe by every engineering metric and still terrify the people around it. A mobile platform that never collides with anyone but barrels past workers at high speed, inches from their bodies, will feel threatening no matter what the collision statistics say. Conversely, a robot that creeps along at a snail’s pace to avoid alarming anyone may be so sluggish that it fails its task entirely. This gap between objective safety and subjective comfort has long been a blind spot in robotics, and a new framework presented in the journal Autonomous Robots aims to close it by letting robots learn, in real time, exactly how fast and how close people are willing to tolerate them.
The framework, called PERSCO, was developed by Sanne van Waveren, Zulfiqar Zaidi, and Matthew Gombolay at the Georgia Institute of Technology. Its central insight is that perceived safety can be treated as a tunable control problem rather than a fixed design choice. The researchers build on control barrier functions, or CBFs, mathematical constructs that guarantee a robot stays within a set of safe states by constraining its control inputs at every time step. Traditional CBFs enforce physical safety with static parameters that never change during an interaction. PERSCO instead parameterizes the CBF with two variables that directly shape how the robot’s behavior feels to nearby humans: the minimum distance the robot must keep from each person, and the maximum deceleration it is allowed to use, which in turn caps how fast it may approach anyone.
These two parameters have intuitive physical meaning. The distance parameter defines an intimate space around each person that the robot may never enter. The deceleration parameter determines the stopping distance the robot must be able to achieve at its current speed; a robot permitted stronger braking can safely travel faster, because it can halt in a shorter distance. By adjusting the pair, the controller can make the robot behave anywhere from maximally cautious to maximally assertive. The key question is which combination a given person actually perceives as safe, and that is something no designer can hard-code in advance, because perceptions vary widely between individuals and contexts.
To answer it, PERSCO treats the problem as active learning. Each person carries a hidden perceived safety function that maps any parameter pair to a judgment of safe or unsafe. The robot cannot observe this function directly, so it maintains a surrogate model, a classifier trained on feedback, and probes the boundary between safe and unsafe parameter regions. Crucially, the researchers designed the feedback to be as unobtrusive as possible. Rather than asking people to fill out Likert scales mid-task, PERSCO adopts a principle of perceived safe until proven unsafe: humans only signal when they feel uncomfortable, using a simple visual cue, in this study a handheld AprilTag sign raised toward a camera. Silence is treated as implicit safe feedback, provided the robot has logged enough close encounters with that person without any complaint.
The learning algorithm is engineered to minimize how often people must intervene. When unsafe feedback arrives, the robot updates its classifier and selects the next candidate parameters using a novel sampling strategy that balances two criteria: entropy, which targets regions where the model is most uncertain about where the boundary lies, and diversity, which favors candidates far from previously tested ones. Importantly, the sampler only considers parameters the model predicts to be safe, so the robot never deliberately behaves in a way it believes will alarm the human. When no unsafe feedback arrives after repeated close encounters, the robot gradually relaxes its parameters, stepping toward the least restrictive pair on the safety boundary, which maximizes task efficiency while remaining at the edge of what the person tolerates.
All of this runs inside a model predictive control loop with a 0.1-second time step, where the perceived safety CBF is enforced over the entire planning horizon while accounting for predicted human motion. A separate, unchanging physical safety CBF with the most aggressive parameters guarantees collision avoidance at all times, so no matter how the learned parameters evolve, the robot can always brake to a standstill before reaching anyone. When parameter updates suddenly tighten the constraints and the robot temporarily finds itself outside the new safe set, a gradual recovery strategy using a slack variable steers it back smoothly; in simulation this reduced jerk by 25 percent and angular acceleration by a factor of 3.6 compared to abrupt corrections, avoiding the jarring sidesteps that quick recovery methods would produce.
Simulation experiments validated the technical choices. Among three candidate classifiers, a support vector classifier with a radial basis function kernel proved the clear winner, updating in about 1.3 milliseconds on average, fast enough for real-time control, while the neural network and Gaussian process alternatives exceeded the control loop’s time budget. Against a battery of classical active learning baselines and black-box optimizers, including multi-armed bandits and Bayesian optimization, PERSCO sampling achieved high accuracy in recovering ground-truth safety parameters while producing the lowest ratio of unsafe feedback events. In a simulated workplace with three moving pedestrians, the system converged to near-optimal parameters in roughly eight minutes, both with and without noise injected into the feedback.
The decisive test came with real humans. Fifty-four participants, organized into eighteen groups of three, performed a workplace-inspired assembly task, walking between workstations to place LED pins on breadboards while a Boston Dynamics Spot robot navigated the space autonomously, covering 7,074 meters over the course of the study. Participants experienced three conditions: individual adaptation, in which the robot learned separate parameters for each person; collective adaptation, in which one shared parameter set was updated from anyone’s feedback; and an adversarial condition, in which the robot responded to feedback by becoming more aggressive rather than more cautious. The adversarial condition served as a control to test whether adaptation itself, or only feedback-aligned adaptation, improves how safe people feel.
The results were striking. Both aligned conditions significantly outperformed the adversarial one on perceived safety, comfort, and anxiety, all with p-values below .001 and large effect sizes. Collective adaptation scored highest overall, and participants raised their feedback signs significantly less often under collective updates, suggesting that people benefit from feedback provided by their teammates. A mediation analysis revealed that the effect of condition on perceived safety was fully mediated by the average size of the robot’s safety boundary, meaning the psychological benefit flowed directly from the geometric changes in the robot’s enforced constraints. Notably, individual adaptation offered no task-performance advantage over collective adaptation, contrary to the researchers’ hypothesis, possibly because fewer parameter changes allowed the robot to plan more consistently.
The authors are candid about limitations: the study took place in a controlled environment, sessions were capped at twelve minutes so parameters did not always converge, and treating silence as safe feedback assumes people are attentive enough to complain when they feel threatened. Still, the work marks a meaningful shift in how roboticists think about safety. By framing perceived safety as a quantity that can be measured, learned, and optimized alongside task performance, PERSCO argues that true safety encompasses psychological well-being, not just the absence of collisions. As robots move into warehouses, hospitals, and factories, the systems that earn human trust may be the ones that ask, in effect, how their presence feels, and adjust accordingly.
Subject of Research: Perceived-safe control of mobile robots using active learning from human feedback
Article Title: PERSCO: Perceived safe control of mobile robots in human groups with active learning
Article References: van Waveren, S., Zaidi, Z., & Gombolay, M. (2026). PERSCO: Perceived safe control of mobile robots in human groups with active learning. Autonomous Robots, 50(4), Article 43. https://doi.org/10.1007/s10514-026-10262-7
Image Credits: AI Generated
DOI: 10.1007/s10514-026-10262-7
Keywords: perceived safety, human-robot interaction, control barrier functions, active learning, mobile robots, model predictive control, social robotics, human-aware motion planning, adaptive control, Boston Dynamics Spot, user study, machine learning
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Tags: active learningadaptive controladaptive robot speed control based on human proximitybalancing safety and efficiency in autonomous systemsBoston Dynamics Spotcontrol barrier functionscontrol barrier functions in roboticsdynamic safety control in roboticshuman-aware motion planninghuman-centered robot navigationhuman-robot interactionhuman-robot interaction comfortimproving robot acceptance in shared workspacesMachine learningmobile robotsmodel predictive controlperceived safetyPERSCO framework for robot speed and distance tuningreal-time robot behavior adaptationreal-time safety learning algorithmsRobotics safety perceptionsocial roboticssubjective safety versus objective safety in automationuser study
