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# Human-Robot Interaction

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  1. arXiv

    Construction robots drop hand-written avoidance rules: LiDAR-only corrections cut bricklaying path irregularity by 39.2%

    Construction robots working near people must anticipate human motion, yet existing social-navigation methods rely on hand-crafted interaction rules, labeled demonstrations, or robot-specific control policies. This LiDAR-only framework generates residual targets automatically by constant-velocity extrapolation, models relations among robot, pedestrians and goal with a graph neural network, and uses an extended Kalman filter to turn frame-wise detections into dynamic graphs, outputting planner-agnostic path corrections. In a bricklaying trial it reduced path irregularity by 39.2% and raised minimum time-to-collision; across seven Dynablox sequences it raised average minimum time-to-collision by 38.3% with shorter, smoother paths than the baselines.

    Yilong Chen（First author）
    Graph Neural NetworkSim-to-Real TransferSocially Aware Robot NavigationLiDAR-Based Dynamic Object TrackingHuman-Robot Interaction
  2. researchsquare.com

    Emotion-reading robots can be fooled: attacked accuracy falls below 0.16 while confidence stays at 0.964

    Facial emotion recognition for human-robot interaction is usually judged by clean-set accuracy, yet a robot that maps an emotion estimate to an action also needs to know whether that estimate can be trusted. Comparing five pipelines on a five-class FER2013 subset, the strongest deep residual CNN reaches 0.760 accuracy and 0.753 macro-F1 but collapses with every other trained architecture under gradient attacks: FGSM drops all four below 0.16 accuracy at epsilon 0.01, and PGD drives them near zero at epsilon 0.02 and above. The attacked predictions stay confident, with mean confidence 0.964 under PGD and 96.0% still above a 0.70 threshold after temperature scaling, so thresholding the maximum softmax probability does not separate legitimate from adversarial predictions.

    University of BanguiUniversity of NgaoundéréUniversity of Brighton
    Ezekiel Olayide Tolulope（First author）
    Facial Emotion RecognitionAdversarial RobustnessConfidence CalibrationHuman-Robot Interaction
  3. arXiv

    REALM adds a delay prior and stochastic expression refinement to turn listening from a frozen stare into blinks and smiles on a robot

    REALM is a coarse-to-fine framework for audio-driven reactive listening that fuses listener motion history with speaker audio through a Reactive Gated Speaker–Listener Fusion module using a delay-centered attention prior and adaptive gating, then predicts a base motion trajectory with a coarse decoder and augments it with audio-conditioned stochastic residuals in the expression subspace, improving over the evaluated baselines on multiple motion-quality metrics on ViCo and L2L and deploying to an Ameca humanoid robot with a perceptual user study.

    University of North TexasMacquarie University
    Peizhen Li（First author）
    REALMReactive Gated Speaker-Listener FusionCoarse-to-Fine Stochastic RefinementAudio-Driven Listener Motion GenerationHuman-Robot Interaction
  4. arXiv

    In-Context Robot Learning with General-Purpose VLM Agents: The GPT-Policy Framework

    This work introduces GPT-Policy, a framework that connects an off-the-shelf general-purpose vision-language model (VLM) to robot tools through a shared context-to-action closed loop, and evaluates its in-context learning on real robots across five context families (human videos, robot videos with actions, goal images, self-interaction history, and online human-robot interaction), finding that task-relevant context can improve success while reducing decisions and execution time, yet better task understanding does not ensure precise contact, reliable outcome verification, or physical safety.

    Dongzhou Cheng（First author）
    GPT-PolicyRobotic In-Context LearningGPT-6 AstraRobot ManipulationHuman-Robot Interaction

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