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Related Experiment Video

Updated: Jun 27, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Modular Framework for Responsive and Explainable Robotic Assistance with Intention Prediction Using Human-Centric

Usman Asad1, Azfar Khalid2, Waqas Akbar Lughmani3

  • 1Department of Mechanical Engineering, Capital University of Science and Technology, Islamabad 45750, Pakistan.

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

The Agentic Unified Robotic Assistance (AURA) Framework enhances human-robot collaboration by using Large Language Models (LLMs) and specialized monitors for proactive assistance. This system improves intent prediction and decision-making in real-time robotic tasks.

Keywords:
digital twinsexplainable AIhuman–robot collaborationindustry 5.0intent predictionproactive assistancevision-language models

Related Experiment Videos

Last Updated: Jun 27, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Human-Robot Interaction

Background:

  • Proactive robotic assistance in human-robot collaboration (HRC) is crucial for seamless workflow integration.
  • Existing systems often struggle to perceive context, anticipate needs, and intervene appropriately without disruption.

Purpose of the Study:

  • To introduce the Agentic Unified Robotic Assistance (AURA) Framework for proactive robotic assistance.
  • To develop a reconfigurable and auditable system for HRC without retraining.

Main Methods:

  • Coupling Large Language Model (LLM) reasoning with Standard Operating Procedures (SOPs).
  • Utilizing a modular layer of specialized monitors (Intent, Motion, Perception, Sound, Affordance, Performance).
  • Implementing a human-in-the-loop data collection and an offline evaluation scheme with an Appropriateness Score (A-Score).

Main Results:

  • Progressive gains in intent prediction and decision-making with richer grounded context.
  • Combined F1 scores improved by over 20 points between context-poor and context-rich conditions.
  • Lightweight models performed comparably to heavier models with significantly reduced inference latency.

Conclusions:

  • The AURA framework provides a scalable solution for proactive robotic assistance.
  • The developed benchmark dataset and evaluation methodology advance HRC research.
  • Structured grounding enables efficient and effective multimodal reasoning in robotic systems.