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Updated: Jul 30, 2025

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

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Published on: April 21, 2023

Exploring Tactile Temporal Features for Object Pose Estimation during Robotic Manipulation.

Viral Rasik Galaiya^1,2, Mohammed Asfour², Thiago Eustaquio Alves de Oliveira³

¹Robotics and AI Lab, Department of Computer Science, Memorial University of Newfoundland and Labrador, St. John's, NL A1C 5S7, Canada.

Sensors (Basel, Switzerland)

|May 13, 2023

Summary

This summary is machine-generated.

This study shows that using temporal information from tactile sensors significantly improves robotic object orientation estimation. A specific window of sensor readings, not larger sizes, offers the best performance for dexterous manipulation tasks.

Keywords:

LSTM object manipulation pose estimation sliding window tactile sensing

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Area of Science:

Robotics
Artificial Intelligence
Sensor Technology

Background:

Dexterous robotic manipulation requires accurate object state estimation, particularly orientation.
Traditional visual methods for pose estimation face challenges with occlusions.
Tactile sensing offers a robust alternative, with recent focus on temporal data.

Purpose of the Study:

To explore the utility of temporal information from tactile sensors for estimating grasped object orientation.
To evaluate the impact of different time-window sample sizes on orientation estimation accuracy.
To analyze the performance of neural networks with long short-term memory (LSTM) layers using tactile data.

Main Methods:

Collected tactile data from a compliant tactile sensor.
Utilized neural networks incorporating long short-term memory (LSTM) layers.
Experimented with various time-window sample sizes for sensor readings.

Main Results:

Temporal information from tactile sensors demonstrably improved object angle estimation.
An optimal window size of 40 samples yielded a mean absolute error (MAE) of 0.0375 radians.
Larger window sizes did not provide further performance enhancements.

Conclusions:

Temporal data from tactile sensors is beneficial for robotic pose estimation.
The analysis of window size performance provides a foundation for future tactile sensing research.
This approach can enhance underactuated robotic designs and complement visual pose estimation.