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Summary
This summary is machine-generated.

A new fabric tactile interface enhances robotic grasping with millisecond feedback. This system improves underwater robot navigation and object manipulation by fusing visual and tactile data for advanced machine intelligence.

Keywords:
cross‐modal sensingembodied intelligenceevent‐driven sensorself‐adaptive graspingvisual–tactile fusion

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Area of Science:

  • Robotics and Artificial Intelligence
  • Materials Science and Engineering
  • Sensor Technology

Background:

  • Current embodied intelligent agents struggle with natural environmental interactions due to limitations in unimodal sensing and cross-modal coordination, especially in dynamic, contact-rich scenarios.
  • Dexterous manipulation of objects, particularly fragile or slippery ones, remains a significant challenge for robotic systems.
  • Underwater operations present unique challenges for robots, where visual perception is often limited, necessitating alternative sensing modalities.

Purpose of the Study:

  • To develop a highly sensitive, robust, and event-driven tactile interface for improved robotic perception and manipulation.
  • To create a bio-inspired visual-tactile fusion (VTF) architecture for enhanced underwater robotic capabilities.
  • To establish a closed-loop cross-modal perception-decision system for advanced autonomous decision-making in intelligent agents.

Main Methods:

  • Fabrication of a novel fabric-based tactile interface with a woven structure and cross-fiber electrodes, enabling event-driven, dual-mode pressure/slip feedback.
  • Development of a bio-inspired visual-tactile fusion (VTF) architecture integrating tactile data with visual semantics for decision-level optimization.
  • Implementation of a closed-loop system linking environmental interaction, cross-modal perception, and autonomous decision-making for humanoid machine intelligence.

Main Results:

  • The tactile interface demonstrated breakthroughs in sensitivity (246.3 kPa⁻¹) and pressure detection (>450 kPa), along with waterproof robustness.
  • Millisecond-level pressure/slip dual-mode feedback enabled self-adaptive grasping, significantly improving the manipulation of delicate objects.
  • The VTF architecture achieved 97.7% accuracy in complex underwater tasks, including manipulation of transparent objects and recognition of similar items.

Conclusions:

  • The developed fabric-based tactile interface and VTF architecture significantly advance the capabilities of embodied intelligent agents, particularly in dynamic and underwater environments.
  • The event-driven tactile feedback and visual-tactile fusion enhance robotic autonomy, adaptation, and dexterous manipulation, crucial for real-world applications.
  • This work represents a significant step towards creating intelligent robotic systems with superior perception-decision capabilities for open-world scenarios.