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

Tatsuya Teramae,Koji Ishihara,Jun Morimoto,Erhan Oztop,Jan Babič

doi:10.3389/fnbot.2018.00071

Tatsuya Teramae, Koji Ishihara + Show 3 more

Open Access

https://doi.org/10.3389/fnbot.2018.00071

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Abstract

Pneumatically actuated muscles (PAMs) provide a low cost, lightweight, and high power-to-weight ratio solution for many robotic applications. In addition, the antagonist pair configuration for robotic arms make it open to biologically inspired control approaches. In spite of these advantages, they have not been widely adopted in human-in-the-loop control and learning applications. In this study, we propose a biologically inspired multimodal human-in-the-loop control system for driving a one degree-of-freedom robot, and realize the task of hammering a nail into a wood block under human control. We analyze the human sensorimotor learning in this system through a set of experiments, and show that effective autonomous hammering skill can be readily obtained through the developed human-robot interface. The results indicate that a human-in-the-loop learning setup with anthropomorphically valid multi-modal human-robot interface leads to fast learning, thus can be used to effectively derive autonomous robot skills for ballistic motor tasks that require modulation of impedance.

Highlights

Human-in-the-loop control systems provide an effective way of obtaining robot skills that can eliminate the need for time consuming controller design (Peternel et al, 2016)
We propose a multimodal approach to control a Pneumatically Actuated Muscle (PAM) based robot where EMG signals and the elbow angle of the human arm are anthropomorphically mapped to the robot creating an intuitive control scheme
We proposed and realized a biologically valid multimodal human-in-the-loop system on an antagonistically designed pneumatically actuated one link, two artificial muscled robot

Summary

Introduction

Human-in-the-loop control systems provide an effective way of obtaining robot skills that can eliminate the need for time consuming controller design (Peternel et al, 2016). How the human in the loop would adapt and learn to control the PAM based robots has not been investigated earlier. To our knowledge, we make the first attempt toward obtaining of a non-trivial skill for a PAM based robot through human-in-the-loop robot control. Note that we make a distinction between human-in-the-loop control and kinesthetic teaching based studies (Hersch et al, 2008; Kronander and Billard, 2014; Tykal et al, 2016), as in the former human is the learning controller generating motor commands in real-time as opposed to being an active scaffold or a guide to the robot. After skilled operation is achieved by the human, autonomous controller synthesis boils down to mimicking human behavior by the help

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