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Related Concept Videos

Tactile and Chemical Senses01:27

Tactile and Chemical Senses

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Tactile senses encompass touch, temperature, and pain, each mediated by specific receptors. Touch receptors detect mechanical energy or pressure against the skin. Sensory fibers from these receptors enter the spinal cord and relay information to the brain stem. Here, most fibers cross over to the opposite side of the brain. The touch information then moves to the thalamus, which projects a map of the body's surface onto the somatosensory areas of the parietal lobes in the cerebral cortex.
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Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
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Quantitative Assessment of Cortical Auditory-tactile Processing in Children with Disabilities
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Contact Modelling and Tactile Data Processing for Robot Skins.

Wojciech Wasko1, Alessandro Albini2, Perla Maiolino3

  • 1Nvidia Corporation, 83304 Przodkowo, Poland. wojciech@wasko.io.

Sensors (Basel, Switzerland)
|February 21, 2019
PubMed
Summary
This summary is machine-generated.

This study evaluates computational methods for robot tactile sensing, analyzing algorithms for extracting contact event data from robot skin. Findings characterize algorithm performance for enhanced robotic interaction capabilities.

Keywords:
Boussinesq–CerrutiLovecontact modellinginverse contact problemrobot skin

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Area of Science:

  • Robotics
  • Computational Mechanics
  • Sensor Technology

Background:

  • Tactile sensing is crucial for advanced robot behaviors, enabling interaction with humans and environments.
  • Developing effective tactile sensing relies on computational methods for interpreting contact event data.
  • Large-scale capacitance-based robot skin technology has been developed to address these needs.

Purpose of the Study:

  • To analyze the computational aspects of extracting information from contact events using robot skin.
  • To evaluate the performance of classical Boussinesq-Cerruti and Love's approaches for distributed inverse contact problems.
  • To compare algorithm performance using both public datasets and custom robot skin data.

Main Methods:

  • Analysis of Boussinesq-Cerruti's solution for distributed inverse contact problems.
  • Evaluation of Love's approach for distributed inverse contact problems.
  • Performance characterization using a freely available dataset and data from robot skin surfaces.

Main Results:

  • Qualitative and computational performance analysis of Boussinesq-Cerruti and Love's approaches.
  • Characterization of algorithm performance based on experimental data from robot skin.
  • Insights into the effectiveness of different computational methods for tactile sensing.

Conclusions:

  • The study provides a performance characterization of key algorithms for tactile sensing.
  • Findings contribute to the development of more sophisticated robot interaction capabilities.
  • The research validates computational approaches using real-world robot skin data.