Hybrid Attention Estimation Pipeline for Adaptive HRI Using an Expressive Robotic Head

• URL: http://arxiv.org/abs/2608.00284v1
• Date: Fri, 31 Jul 2026 20:42:00 GMT
• Title: Hybrid Attention Estimation Pipeline for Adaptive HRI Using an Expressive Robotic Head
• Abstract summary: This paper presents an applied case study on hybrid visual attention estimation for human-robot interaction using an expressive robotic head based on the InMoov ecosystem.<n>The proposed pipeline combines a fast geometric perception layer with an independent semantic perception layer based on a vision-language model.<n>Results show reliable interaction start across all trials, consistent pause behavior in the adaptive distraction condition, and non-redundant semantic information between the geometric and semantic outputs.
• Score: 1.2236260853766314
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents an applied case study on hybrid visual attention estimation for human-robot interaction using an expressive robotic head based on the InMoov ecosystem. The proposed pipeline combines a fast geometric perception layer with an independent semantic perception layer based on a vision-language model. The geometric layer provides high-frequency face and head-pose information for temporal regulation, while the semantic layer receives only raw egocentric camera frames and produces contextual attention labels related to attention toward the robot, phone use, or attention elsewhere. These signals are integrated through a finite state machine that regulates adaptive interaction behavior, including activation, waiting, interaction resumption, and return to rest. The system was evaluated with 10 participants across 40 trials covering baseline and adaptive interaction conditions. Results show reliable interaction start across all trials, consistent pause behavior in the adaptive distraction condition, and non-redundant semantic information between the geometric and semantic outputs.

Related papers

• Inter-X++: A Comprehensive Benchmark for Multimodal Human-Human Interaction Analysis [55.78822230833247]
  We present Inter-X++, a comprehensive and large-scale benchmark designed to empower versatile HHI analysis.<n>Inter-X++ provides 11,388 high-fidelity interaction sequences and over 8.1M frames, featuring precise whole-body movements and detailed finger articulations.<n>We propose OpenHHI, a single and unified HHI representation and modeling framework that jointly optimize interaction reconstruction and semantic understanding.
  arXiv  Detail & Related papers  (2026-08-20T17:51:48Z)
• BEHAVE: A Hybrid AI Framework for Real-Time Modeling of Collective Human Dynamics [0.0]
  Existing AI systems fail to capture the collective dynamics that determine whether a group remains stable or transitions into escalation or breakdown.<n>We introduce BEHAVE, a formal framework that models collective dynamics as continuous behavioral fields defined over an interaction space.<n>The same fields, recalibrated, apply to crowd safety, crisis-team dynamics, education, and clinical contexts.
  arXiv  Detail & Related papers  (2026-05-12T20:32:02Z)
• SocialMirror: Reconstructing 3D Human Interaction Behaviors from Monocular Videos with Semantic and Geometric Guidance [49.69016078147708]
  Accurately reconstructing human behavior in close-interaction scenarios is crucial for enabling realistic virtual interactions in augmented reality.<n>We propose SocialMirror, a diffusion-based framework that integrates semantic and geometric cues to effectively address these issues.<n>SocialMirror achieves state-of-the-art performance in reconstructing interactive human meshes, demonstrating strong generalization across unseen datasets and in-the-wild scenarios.
  arXiv  Detail & Related papers  (2026-04-15T07:41:52Z)
• Learning Human-Object Interaction as Groups [52.28258599873394]
  GroupHOI is a framework that propagates contextual information in terms of geometric proximity and semantic similarity.<n>It exhibits leading performance on the more challenging Nonverbal Interaction Detection task.
  arXiv  Detail & Related papers  (2025-10-21T07:25:10Z)
• From Biometrics to Environmental Control: AI-Enhanced Digital Twins for Personalized Health Interventions in Healing Landscapes [5.381203326687129]
  This paper presents an AI-enhanced digital twin framework that integrates biometric signals, specifically electrocardiogram (ECG) data, with environmental parameters such as temperature, humidity, and ventilation.<n>The system continuously acquires, synchronises, and preprocesses multimodal data streams to construct a responsive virtual replica of the physical environment.
  arXiv  Detail & Related papers  (2025-05-04T21:58:18Z)
• Embodied World Models Emerge from Navigational Task in Open-Ended Environments [5.785697934050656]
  We ask whether a recurrent agent, trained solely by sparse rewards to solve procedurally generated planar mazes, can autonomously internalize metric concepts such as direction, distance and obstacle layout.<n>After training, the agent consistently produces near-optimal paths in unseen mazes, behavior that hints at an underlying spatial model.
  arXiv  Detail & Related papers  (2025-04-15T17:35:13Z)
• Siamese Network with Dual Attention for EEG-Driven Social Learning: Bridging the Human-Robot Gap in Long-Tail Autonomous Driving [0.0]
  This study presents a brain-computer interface (BCI) framework that enables classification of Electroencephalogram (EEG) signals to detect cognitively demanding and safety-critical events.<n>As a timely and motivating co-robotic engineering application, we simulate a human-in-the-loop scenario to flag risky events in semi-autonomous robotic driving.<n>We propose a dual-attention Siamese convolutional network paired with Dynamic Time Warping Barycenter Averaging approach to generate robust EEG-encoded signal representations.
  arXiv  Detail & Related papers  (2025-04-14T15:06:17Z)
• Visual-Geometric Collaborative Guidance for Affordance Learning [63.038406948791454]
  We propose a visual-geometric collaborative guided affordance learning network that incorporates visual and geometric cues. Our method outperforms the representative models regarding objective metrics and visual quality.
  arXiv  Detail & Related papers  (2024-10-15T07:35:51Z)
• Emotion Recognition from the perspective of Activity Recognition [0.0]
  Appraising human emotional states, behaviors, and reactions displayed in real-world settings can be accomplished using latent continuous dimensions. For emotion recognition systems to be deployed and integrated into real-world mobile and computing devices, we need to consider data collected in the world. We propose a novel three-stream end-to-end deep learning regression pipeline with an attention mechanism.
  arXiv  Detail & Related papers  (2024-03-24T18:53:57Z)
• Disentangled Interaction Representation for One-Stage Human-Object Interaction Detection [70.96299509159981]
  Human-Object Interaction (HOI) detection is a core task for human-centric image understanding. Recent one-stage methods adopt a transformer decoder to collect image-wide cues that are useful for interaction prediction. Traditional two-stage methods benefit significantly from their ability to compose interaction features in a disentangled and explainable manner.
  arXiv  Detail & Related papers  (2023-12-04T08:02:59Z)
• TRiPOD: Human Trajectory and Pose Dynamics Forecasting in the Wild [77.59069361196404]
  TRiPOD is a novel method for predicting body dynamics based on graph attentional networks. To incorporate a real-world challenge, we learn an indicator representing whether an estimated body joint is visible/invisible at each frame. Our evaluation shows that TRiPOD outperforms all prior work and state-of-the-art specifically designed for each of the trajectory and pose forecasting tasks.
  arXiv  Detail & Related papers  (2021-04-08T20:01:00Z)
• Identification of brain states, transitions, and communities using functional MRI [0.5872014229110214]
  We propose a Bayesian model-based characterization of latent brain states and showcase a novel method based on posterior predictive discrepancy. Our results obtained through an analysis of task-fMRI data show appropriate lags between external task demands and change-points between brain states.
  arXiv  Detail & Related papers  (2021-01-26T08:10:00Z)
• Continuous Emotion Recognition via Deep Convolutional Autoencoder and Support Vector Regressor [70.2226417364135]
  It is crucial that the machine should be able to recognize the emotional state of the user with high accuracy. Deep neural networks have been used with great success in recognizing emotions. We present a new model for continuous emotion recognition based on facial expression recognition.
  arXiv  Detail & Related papers  (2020-01-31T17:47:16Z)

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.