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Intelligent Sensing, Conversational Intelligence and Robot Control for Human–Machine Coexistence

A Special Issue of Sensors (ISSN 1424-8220) belonging to the section "Sensors and Robotics".

Deadline for manuscript submissions: 20 December 2026 | Viewed by 549

Editor

Special Issue Information

Dear Colleagues,

As society moves toward seamless coexistence between humans and autonomous agents, the ability of machines to perceive and respond to both verbal and non-verbal human intent is becoming a central challenge in next-generation robotics and sensing research.

A defining feature of this challenge is the need to integrate two fundamentally contrasting visual paradigms: third-person visual sensing—enabling precise sign and gesture recognition from an external observer's perspective; and ego-centric (first-person) perception—enabling robots to infer human intent and understand environmental context from their own viewpoint. This duality reflects a core demand placed on symbiotic robotic systems: to simultaneously observe the human and inhabit the human world.

Recent breakthroughs in Large Language Models (LLMs) have greatly advanced conversational AI; however, a critical gap remains in grounding this high-level linguistic intelligence into reliable, real-time physical robot motion. True human–machine symbiosis, therefore, demands tightly integrated systems that bridge "understanding"—encompassing multimodal sensing and AI reasoning—and "acting"—encompassing compliant, vision-informed motor control—into a unified, embodied architecture.

For this Special Issue, we invite the contribution of original research articles and reviews that advance the frontier of intelligent, symbiotic human–robot systems across sensing, reasoning, and actuation. We welcome contributions that address these challenges from both fundamental and applied perspectives.

  • Multimodal Conversational AI: Grounding LLMs with visual, tactile, and contextual sensing for natural human–robot dialogue.
  • Vision-based Interaction: Third-person sign language and gesture recognition; ego-centric video analysis for intent estimation and context awareness.
  • Human–Robot Symbiosis: Motion analysis, social relationship modeling, and proactive response in human-centric environments.
  • Advanced Robot Control: Bio-inspired motor control, compliant actuation, and real-time vision-informed control loops.
  • Social and Clinical Applications: Conversational robots in healthcare, elderly care, psychiatry, and associated ELSI considerations.
  • System Integration: Hardware–software co-design and real-time architectures for embodied symbiotic interaction.

Prof. Dr. Duk Shin
Guest Editor

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Keywords

  • conversational AI
  • large language models
  • ego-centric vision
  • sign gesture recognition
  • human–robot interaction
  • robot motor control
  • human–machine coexistence
  • multimodal sensing

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Published Papers (1 paper)

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Research

16 pages, 2543 KB  
Article
Sensor-Based Assessment of Upper-Limb Motor Control in Children and Adults Using Two-Dimensional Circular Target Tracking
by Yohan Song, Jihun Kim, Jongho Lee and Jaehyo Kim
Sensors 2026, 26(17), 5344; https://doi.org/10.3390/s26175344 - 24 Aug 2026
Viewed by 257
Abstract
Portable sensor-based assessment of human motion is important for monitoring motor development and informing the design of rehabilitation applications. This study investigated upper limb motor characteristics during two-dimensional circular target tracking across different age groups and speeds. Fifty-one participants were divided into three [...] Read more.
Portable sensor-based assessment of human motion is important for monitoring motor development and informing the design of rehabilitation applications. This study investigated upper limb motor characteristics during two-dimensional circular target tracking across different age groups and speeds. Fifty-one participants were divided into three groups: lower-elementary children, upper-elementary children, and adults. Using a tablet- and stylus-based input device suitable for human–computer interaction systems, participants performed the tracking task at three speeds. Each trial included relatively feedback-dominant target-visible segments and relatively feedforward-dominant temporarily target-invisible segments. The latter imposed greater demands on predictive tracking because current target-related visual information was unavailable. A participant-level Tracer Error Ratio was calculated as the target-invisible positional error divided by the target-visible positional error. The two child groups showed similar ratios across all speeds, whereas adults showed lower ratios than either child group. As tracking speed increased, the ratio decreased in all groups, with the adult mean falling below 1 only in the high-speed condition. These findings indicate an adult-child difference in predictive tracking during temporary target disappearance but do not directly identify a specific cerebellar mechanism. This portable tablet-stylus approach may be useful for research on age group differences in upper limb target tracking performance. Full article
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