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Related Experiment Video

Updated: Feb 2, 2026

Rapid Manufacturing of Thin Soft Pneumatic Actuators and Robots

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Published on: November 8, 2019

Human-In-The-Loop Control and Task Learning for Pneumatically Actuated Muscle Based Robots.

Tatsuya Teramae¹, Koji Ishihara¹, Jan Babič²

¹Department of Brain Robot Interface, ATR, CNS, Kyoto, Japan.

Frontiers in Neurorobotics

|November 22, 2018

Summary

This summary is machine-generated.

Pneumatically actuated muscles (PAMs) enable low-cost, lightweight robots. This study shows a human-robot interface allows fast learning of hammering skills, demonstrating effective human-in-the-loop control for robotic applications.

Keywords:

biologically inspired multimodal control electromyography human in the loop control human motor learning pneumatically actuated muscle

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Area of Science:

Robotics
Human-Robot Interaction
Biologically Inspired Engineering

Background:

Pneumatically actuated muscles (PAMs) offer advantages like low cost and high power-to-weight ratio for robotic systems.
Despite benefits, PAMs are underutilized in human-in-the-loop control and learning applications.
Antagonist pair configurations in robotic arms facilitate biologically inspired control strategies.

Purpose of the Study:

To propose and evaluate a biologically inspired multimodal human-in-the-loop control system for robotic applications.
To investigate human sensorimotor learning within this system.
To demonstrate the acquisition of autonomous hammering skills through human control.

Main Methods:

Development of a novel human-robot interface for controlling a one degree-of-freedom robot.
Implementation of a biologically inspired control system utilizing PAMs.
Conducting experiments to analyze human sensorimotor learning during a hammering task.

Main Results:

Effective autonomous hammering skills were readily acquired through the developed human-robot interface.
The human-in-the-loop learning setup facilitated rapid skill acquisition.
The system demonstrated the potential for deriving autonomous robot skills for ballistic motor tasks.

Conclusions:

A multimodal human-robot interface combined with human-in-the-loop learning enables efficient skill acquisition in robots.
Biologically inspired control approaches are effective for tasks requiring impedance modulation.
This approach accelerates the development of autonomous robot skills for complex motor tasks.