Demonstrating Naviarm: Augmenting the Learning of Motor
Skills using a Backpack-type Robotic Arm System
Azumi Maekawa∗

Shota Takahashi∗

MHD Yamen Saraiji

The University of Tokyo
Meguro-ku, Tokyo, Japanazumi@star.rcast.u-tokyo.ac.jp

Waseda University
Shinjuku, Tokyo, Japans.tbb1994-estl@asagi.waseda.jp

Keio University Graduate School ofMedia Design
Kohoku-ku, Kanagawa, Japanyamen@kmd.keio.ac.jp

Sohei Wakisaka

Hiroyasu Iwata

Masahiko Inami

The University of Tokyo
Meguro-ku, Tokyo, Japan
wakisaka@star.rcast.u-tokyo.ac.jp

Waseda University
Shinjuku, Tokyo, Japanjubi@waseda.jp

The University of Tokyo
Meguro-ku, Tokyo, Japaninami@star.rcast.u-tokyo.ac.jp

ABSTRACT1

We present a wearable haptic assistance robotic system for augmented motor learning called Naviarm. This system comprises
two robotic arms that are mounted on a user’s body and are used
to transfer one person’s motion to another offline. Naviarm prerecords the arm motion trajectories of an expert via the mounted
robotic arms and then plays back these recorded trajectories toshare the expert’s body motion with a beginner. The Naviarm system is an ungrounded system and provides mobility for the user toconduct a variety of motions. In our demonstration, the user will
experience the recording of arm movement with backpack-type
robotic arm. Then, the recorded movement will replayed and theuser can experience the haptic feedback.

In this paper, we propose Naviarm, a haptic assistance system for theaugmented learning of motor skills that allows the user to recordand play over a relatively wide motion range with the mobility.
Naviarm is a backpack-type robotic arm device with seven DoFs
per arm (Figure 1). In this system, the motion trajectories of anexpert’s arms relative to the expert’s torso are recorded. The target
user of this system is a beginner who aims to learn motor skills
from the expert. The user can play back the prerecorded motion
trajectories via the robotic arms as a learning support.
Our contributions are summarized as follows.

INTRODUCTION• We propose a wearable backpack-type haptic device withdual robotic arms.
• We develop a haptic assistance system for augmented learning of motor skills including mobility and a wide range ofmotion.

CCS CONCEPTS• Human-centered computing → Haptic devices; Human computer interaction (HCI); • Computer systems organization →Robotics;

2KEYWORDSSYSTEM OVERVIEW

In this section, we present a wearable haptic guidance system forlearning arm motor skills. The Naviarm system provides haptic
feedback for the wrist position trajectory from experts to beginners
using the medium of a wearable robotic arm. Our system for skill
learning is composed of backpack robotic arms, haptic-feedback
wristbands, and a record-and-replay system.

Augmented learning; Wearable Device; Motor Learning; Haptics;
Robotics;
ACM Reference Format:
Azumi Maekawa, Shota Takahashi, MHD Yamen Saraiji, Sohei Wakisaka,Hiroyasu Iwata, and Masahiko Inami. 2019. Demonstrating Naviarm: Augmenting the Learning of Motor Skills using a Backpack-type Robotic Arm
System. In Augmented Human International Conference 2019 (AH2019), March
11–12, 2019, Reims, France. ACM, New York, NY, USA, 2 pages. https://doi.
org/10.1145/3311823.3313868

2.1

Backpack-type Robotic Arms

The hardware design of the backpack dual-arm robot is shown inFigure 1(b). The robot can be carried on the back and has a total
weight of 5.75 kg. Each arm has six DoFs and a length of 787 mm.
Each joint of the robotic arms is driven by servo-motors (KondoB3M-SC-1070-A and B3M-SC-1170-A). The maximum torque ofthe shoulder joint is 15.2 N m, and the resolution is 0.088◦ . The
proposed robotic arm sufficiently covers the range of motion ofa human arm [1] and is longer than the average human arm [2].
Therefore, it does not limit the movement of the wearer’s arm
motion and maintains the DoFs necessary to learn a motor skill.
For details on the design of the robotic arm, please refer to [3].

∗ Both authors contributed equally to this research.

Permission to make digital or hard copies of part or all of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page. Copyrights for third-party components of this work must be honored.
For all other uses, contact the owner/author(s).
AH2019, March 11–12, 2019, Reims, France© 2019 Copyright held by the owner/author(s).
ACM ISBN 978-1-4503-6547-5/19/03.
https://doi.org/10.1145/3311823.3313868
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AH2019, March 11–12, 2019, Reims, FranceMaekawa and Takahashi et al.
wearer and the expert can perform the motor skill performance inthe same way as when the robotic arm is not worn.
In the replay phase, the robotic arm is set to active control, andtherefore, all motors are activated. The arms move the beginner’s
wrists according to the sequential angle data of the recorded expert
motor skill movement. In this phase, the recorded data are also
queried at 100 Hz; therefore, the temporal resolution of the motion
is 0.01 sec. At that time, a beginner can learn the sequential motion
trajectory of the expert while a directed force is applied to the wrist
via the wristband.

2.4

Figure 1: (a) A system overview of the wearable haptic guidance system called Naviarm. (b) The hardware design of thebackpack-type robotic arms. (c) The wristband to provide
haptic feedback from the robot to the wearer.

2.2

3

Haptic-Feedback Wristband

CONCLUSIONSIn this paper, we proposed Naviarm, a novel haptic feedback system for augmenting the learning of motor skills. The system uses
backpack-type robotic arms to transmit haptic information andcan handle a larger range of motion compared to previous haptic
guidance systems.

We designed a custom wristband that connects the end effector of
the robotic arm to the wearer’s wrist (Figure 1(c)). This wristband
consists of 3D printed parts, bearings, and a cloth band. Its total
weight is 91 g, and the cloth band can be applied regardless ofbody differences. The bearing gives a one-DoF passive joint to thewristband. The proposed number of DoFs was chosen because ourfeasibility study indicated that a seven-DoF robotic arm including
an end effector is suitable for skill extraction from an expert andskill transmission to a beginner.

2.3

Naviarm System

Here, we present the main characteristics of the developed backpacktype robotic arm system for learning motor skills. First, the proposed Naviarm system can directly teach the temporal aspectsof a motor skill, e.g., timing, pattern, or sequence, via the dualrobotic arms. During the acquisition of a motor skill, it is challenging to learn sequential motor skills as a structure that smoothly
connects individual motor skills [4]. However, the Naviarm recordand-replay system first assists in conveying an expert’s motor skills
without sequential changes. Therefore, the wearer is able to learna sequential motor skill with motions involving both entire upper
limbs via direct haptic feedback. Then, the proposed backpack-type
robotic arms have higher mobility compared to grounded apparatuses. Mobility is essential for training a wide range of upper
limb motor skills while involving the movement of a user’s legs,
for example, for activities such as dancing and sports. Therefore,
the mobility of Naviarm is an important characteristic in terms ofthe variety of applicable motor skills. Finally, Naviarm can be used
for learning motor skills both online and offline. As an example
of online use, using Naviarm, one teacher would be able to teachsequential dance movements to numerous students in real time.

ACKNOWLEDGMENTS
We would like to thank G. Ganesh, M. Kitazaki, M. Sugimoto, andY. Miyawaki for useful advice. This project was supported by JST
ERATO Grant Number JPM-JER1701, Japan.

Record-and-Replay System

REFERENCESThe Naviarm system comprises a recording phase for the expert
motion trajectory and a replay phase. First, in the recording phase,an expert carries the robot arms and a haptic-feedback wristband isattached to each of the expert’s wrists. When the expert performs
an arm motor skill movement, the joint angle data of each robot arm
are sequentially recorded as the 3D trajectory of the wrist positions
with respect to the wearer’s torso. These angle data are translated
into wrist position trajectories expressing the sequential motor skillof the expert. The sampling frequency of the system is set to 100 Hz.
During the recording phase, the motors of the robotic arms are set
to free rotation so that the system does not add extra force to the[1] National Institution of Technology and Evaluation. 2013. Human Characteristics
Database. (2013). http://www.tech.nite.go.jp/human/[2] Research Institute of Human Engineering for Quality Life. 2008. Japanese Anthropometric Database 2004-2006. (2008). https://www.hql.jp/database/cat/size/size2004[3] MHD Yamen Saraiji, Tomoya Sasaki, Kai Kunze, Kouta Minamizawa, and MasahikoInami. 2018. MetaArms: Body Remapping Using Feet-Controlled Artificial Arms.
In The 31st Annual ACM Symposium on User Interface Software and Technology.
ACM, 65–74.
[4] Misako Sawada, Shiro Mori, and Motonobu Ishii. 2002. Effect of metaphoricalverbal instruction on modeling of sequential dance skills by young children. Perceptual and motor skills 95, 3_suppl (2002), 1097–1105.

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