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Tactile and Chemical Senses

Tactile and Chemical Senses

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. This...

Somatosensation

Somatosensation

The somatosensory system relays sensory information from the skin, mucous membranes, limbs, and joints. Somatosensation is more familiarly known as the sense of touch. A typical somatosensory pathway includes three types of long neurons: primary, secondary, and tertiary. Primary neurons have cell bodies located near the spinal cord in groups of neurons called dorsal root ganglia. The sensory neurons of ganglia innervate designated areas of skin called dermatomes.

Sensory Modalities

Sensory Modalities

Sensation typically is the process by which the sensory receptors and sense organs detect stimuli from the internal and external environment and transmit this information to the central nervous system for processing.
General senses refer to the broad category of sensory information detected by receptors in the body and can be further grouped into somatic and visceral senses. Somatic sensations include touch, pressure, temperature, and pain and are essential for navigating our environment and...

Sensory Perception: Organization of the Somatosensory System

Sensory Perception: Organization of the Somatosensory System

The somatosensory system is the central and peripheral nervous system component that senses and processes touch, pressure, pain, temperature, and body position or proprioception. The process of sensation takes place at three levels:
The receptor level:
The receptor level is the first stage of sensation. It involves the detection of a stimulus by specialized sensory receptors. The stimulus must arrive within the receptor's receptive field. Next, the receptor converts the energy of the stimulus...

Design Example: Resistive Touchscreen

Design Example: Resistive Touchscreen

A device engineer plays a crucial role in designing user interfaces for mobile devices. One such interface is the resistive touchscreen, which fundamentally consists of two metallic layers: a flexible upper layer and a rigid lower layer, separated by a narrow gap. The high resistance between these two layers is a key characteristic of this design.
When a user touches the screen, the two layers make contact at a specific point known as the touchpoint. This contact reduces the resistance between...

Somatosensory, Motor, and Association Cortex

Somatosensory, Motor, and Association Cortex

The somatosensory cortex in the parietal lobes is crucial for interpreting sensory data such as touch, temperature, and proprioception. The somatosensory cortex, situated in the parietal lobes, plays a vital role in interpreting sensory information like touch, temperature, and proprioception—awareness of body position. This specialized brain region features an organized structure wherein neurons at the top primarily process sensations originating from the lower body. In contrast, those at the...

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Related Experiment Video

Updated: Jul 12, 2026

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

TouchWGNN: spatio-temporal tactile perception for multimodal dexterous manipulation.

Yu Ning¹, FuQiang Zhao¹, Qian Liu¹

¹Wireless Multimedia Technology Lab, School of Computer Science and Technology, Dalian University of Technology, Dalian, China.

Frontiers in Robotics and AI

|July 11, 2026

Summary

This study introduces TouchWGNN, a novel framework for robotic hands that uses tactile sensing, vision, and proprioception for dexterous manipulation. Integrating multimodal sensory data significantly improves object-state estimation and manipulation performance.

Keywords:

dexterous manipulation force and tactile sensing graph neural networks object pose estimation reinforcement learning

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Tactile Semiautomatic Passive-Finger Angle Stimulator (TSPAS)

Tactile Semiautomatic Passive-Finger Angle Stimulator (TSPAS)

Published on: July 30, 2020

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Last Updated: Jul 12, 2026

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

Tactile Semiautomatic Passive-Finger Angle Stimulator (TSPAS)

Tactile Semiautomatic Passive-Finger Angle Stimulator (TSPAS)

Published on: July 30, 2020

Area of Science:

Robotics
Artificial Intelligence
Sensor Fusion

Background:

Dexterous in-hand manipulation is crucial for robots but challenging due to occlusions and complex dynamics.
Extracting useful information from tactile sensing and integrating it with other senses remains a significant hurdle.

Purpose of the Study:

To develop a multimodal framework, TouchWGNN, for enhanced object-state estimation in dexterous manipulation.
To leverage tactile sensing by modeling it as a spatio-temporal graph.

Main Methods:

Developed a low-cost tactile sensor array for a five-fingered robotic hand.
Constructed a tactile graph representing contact points (taxels) and their spatial/force features.
Employed a graph-based spatial encoder and temporal module for contact geometry analysis.
Fused tactile estimates with vision and proprioception for reinforcement learning-based policy optimization.

Main Results:

Demonstrated real-time acquisition of normal forces from 113 sensing points.
Showcased improved object-state estimation through spatio-temporal tactile graph modeling.
Achieved superior performance in cube reorientation and Baoding ball swapping tasks compared to unimodal approaches.

Conclusions:

The TouchWGNN framework effectively integrates multimodal sensory data for dexterous manipulation.
Spatio-temporal modeling of tactile signals enhances object-state estimation accuracy.
Multimodal sensing significantly outperforms unimodal sensing in complex robotic manipulation tasks.