Table Of ContentHUMAN INSPIRED
DEXTERITY
IN ROBOTIC
MANIPULATION
HUMAN INSPIRED
DEXTERITY
IN ROBOTIC
MANIPULATION
Edited by
TETSUYOU WATANABE
KENSUKE HARADA
MITSUNORI TADA
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CONTRIBUTORS
JumpeiArata
DepartmentofMechanicalEngineering,FacultyofEngineering,KyushuUniversity,
Fukuoka,Japan
Ji-HunBae
RoboticsR&DGroup,KoreaInstituteofIndustrialTechnology,Cheonan,SouthKorea
QiushiFu
MechanicalandAerospaceEngineering,UniversityofCentralFlorida,Orlando,
FL,UnitedStates
IgorGoncharenko
CollegeofInformationScienceandEngineering,RitsumeikanUniversity,Kusatsu,Japan
KensukeHarada
GraduateSchoolofEngineeringScience,OsakaUniversity,Toyonaka,Japan
AkihiroKawamura
DepartmentofAdvancedInformationTechnology,KyushuUniversity,Fukuoka,Japan
MakikoKouchi
HumanInformaticsResearchInstitute,NationalInstituteofAdvancedIndustrialScienceand
Technology,Tokyo,Japan
VictorKryssanov
CollegeofInformationScienceandEngineering,RitsumeikanUniversity,Kusatsu,Japan
RyutaOzawa
DepartmentofMechanicalEngineeringInformatics,MeijiUniversity,Kawasaki,Japan
MarcoSantello
SchoolofBiologicalandHealthSystemsEngineering,ArizonaStateUniversity,Tempe,AZ,
UnitedStates
MikhailSvinin
CollegeofInformationScienceandEngineering,RitsumeikanUniversity,Kusatsu,Japan
MitsunoriTada
HumanInformaticsResearchInstitute,NationalInstituteofAdvancedIndustrialScienceand
Technology,Tokyo,Japan
KenjiTahara
DepartmentofMechanicalEngineering,KyushuUniversity,Fukuoka,Japan
TetsuyouWatanabe
FacultyofMechanicalEngineering,InstituteofScienceandEngineering,Kanazawa
University,Kanazawa,Japan
ix
x Contributors
ZheXu
DepartmentofMechanicalEngineering,YaleUniversity,NewHaven,CT,UnitedStates
MotojiYamamoto
DepartmentofMechanicalEngineering,KyushuUniversity,Fukuoka,Japan
CHAPTER 1
Background: Dexterity in Robotic
Manipulation by Imitating Human
Beings
Tetsuyou Watanabe
FacultyofMechanicalEngineering,InstituteofScienceandEngineering,
KanazawaUniversity,Kanazawa,Japan
Contents
1.1 Background 1
1.2 ComplementalInformation 3
1.2.1 StatisticallySignificantDifference 3
1.2.2 StateSpaceRepresentation 4
1.2.3 MechanicalImpedance 5
1.2.4 FundamentalGraspingStyle 5
1.2.5 KinematicsandStaticsofRobots 6
1.2.6 DynamicsofRobots 6
References 7
1.1 BACKGROUND
Humanbeingshavebeenakindoftextbookforconstructingrobotsbecause
robots perform the tasks that humans do in the support of human beings.
However, there are a lot of unrevealed things about human beings. Even
if knowledge is given, how to utilize that knowledge during the develop-
ment of robots is on a case by case basis and there are no general method-
ologies to effectively transfer the knowledge during the development
process. It is impossible to deal with all aspects of human beings. This text
is focused on dexterity and aims to understand how human beings acquire
dexterityinobjectmanipulation,discussesthepossibilityofitsapplicationin
roboticsystems,anddrawskeystrategiesfordealingwithroboticdexterous
manipulation in the next generation.
Here,wearefocusedonup-to-dateresearchabouthumanfunctionsand
the method for transferring the functions to robotic manipulation.
HumanInspiredDexterityinRoboticManipulation ©2018ElsevierInc.
1
https://doi.org/10.1016/B978-0-12-813385-9.00001-7 Allrightsreserved.
2 HumanInspiredDexterityinRoboticManipulation
Human functions: The level of dexterous manipulation by robots is cur-
rently far from that of human beings. What can improve the ability of
robots? One hint might be to understand the approach that human beings
takeindexterityacquisition.Forexample,supposethereisataskofgrasping
and manipulating an unknown object. The shape, softness, weight, and
gravitational center are unknown parameters of the presented object.
Howdohumanbeingsidentify thephysicalparametersandusetheidenti-
fiedparameterstocompletedexterousmanipulation?Howdohumanbeings
learn such procedures? The methodology for the identifying and learning
processofhumanbeingscouldprovidevaluableinsightsfortheconstruction
ofdexterousmanipulationofrobots.Thestructureofthehumanfingerand
handplayanimportantrolefordexterousmanipulation.Itwasacquiredin
the process of evolution. The key structures for dexterity are also valid for
the key structures of robotic hand design.
Methodfor transferring humanfunctions torobotic manipulation:It isbasically
difficulttotransferhumanmanipulationtechniquesorfunctionstorobotic
manipulation, because of the different structures and system architectures.
Oneapproachmightbeimitatingorembeddingkeyfunction/components
inrobotsonebyone.Recentattemptstotransferthetechniquesorfunctions
could give us deep insights in realizing dexterous manipulation of robots.
Such attempts could proceed to creating new strategies to design, control,
andplanforroboticmanipulations,althoughthefunctionsdonotperfectly
coincide with the ones for human beings.
This text will consolidate recent approaches from both viewpoints in
acceleratingthenextdevelopmentsinthedexterousmanipulationofrobots.
Tofacilitatethisunderstanding,therearetwoseparatesectionscorrespond-
ing to the two viewpoints. The first section focuses on human functions
while the second section focuses on transferring the functions to robots.
Chapter 2 provides recent revelations in hand anatomy, which lead to
human functions for dexterous manipulation. Newly discovered functions
giveusnewviewpointsforconstructingroboticmanipulation.Theconcept
of muscle synergy has been utilized for controlling robotic hands, but the
synergydoesnotalwayscorrespondtothatforhumanoranimalevolution.
Chapter 3 presents how human beings learn dexterous manipulation in
the context of sensorimotor functions. The actual functions of human
beings give us different insights to understand dexterous manipulation.
Chapter4presentsatrailofexcitationofamultisensoryillusionofasurgical
robotic system to enhance the dexterity of the control of surgical robotic
systems.Ahintoftheembeddedfeaturesofhumanbehaviorsinthesystem
Background:DexterityinRoboticManipulationbyImitatingHumanBeings 3
can be obtained. Chapter 5 examines human reaching behavior when
manipulating parallel flexible objects and shows that the optimal hand tra-
jectory is composed of a fifth order polynomial (as in the classic minimum
jerk model) and trigonometric terms depending on the natural frequencies
of the system and time movement.
Thesecondsectionprovidesrecentresultsofdesign(Chapters6and7),
control (Chapters 8 and 9), and planning (Chapter 10) for dexterous
robotic manipulation while considering human functions. Chapter 6 pre-
sentsanovelanthropomorphicrobotichanddesign,imitatingthesalientfea-
tures of the human hand. Chapter 7 presents a novel fingertip design
imitatingthestructureofhumanfingers,andarobotichandequippedwith
thefingertip.Bothdesignsgivehintsforconstructingrobotichandsutilizing
humanhandfeatures.Basedonthehumanfunctionofthumbopposability,
Chapter 8 presents a control schema utilizing the concept of passivity.
Chapter9presentsthecontrollerwithconsideringthedifferenceofsensing
timing between visual sensors (low sampling rate) and joint sensors (high
samplingrate).Chapter10presentsaplanningmethodologytomanipulate
objects with two arms like human beings.
1.2 COMPLEMENTAL INFORMATION
To make it easier to understand, complemental information is
provided here.
1.2.1 Statistically Significant Difference
Whenexaminingthedifferencesbetweentwogroups,statisticalhypothesis
testingisused.BothgroupsaresupposedtoberepresentedbytheStudent’s
t-distribution. The testing outputs the P-value (probability value), which
indicates the probability for the null hypothesis that each element of the
two groups belongs to the same distribution. If the P-value is less than
thegivenlevelofsignificance(forexample,0.05or5%),thenullhypothesis
is rejected, and it can be said that there is a statically significant difference
between the two groups. This analysis is valid for the two groups. If the
numberofthetargetgroupsismorethantwo,theposthoctestisperformed
for the analysis. Table 1.1 shows a summary of which test should be per-
formed in each case. For details, please see text books on statistics, for
example [1].
4 HumanInspiredDexterityinRoboticManipulation
Table1.1 Whichstatisticaltestshouldbeperformedineachcase
Numberof Assumptionof Assumption
Nameof Target elementsin Distributionin ofvariancein
test comparison eachgroup eachgroup eachgroup
T-test Twogroups No limitation Nothing Nothing
Tukey- Pairwise No limitation Normal Homogeneity
Kramer differences
Bonferoni/ Pairwise Same Normal Homogeneity
Dunn differences
Scheffe No limitation No limitation Nothing Nothing
1.2.2 State Space Representation
State space representation is conducted when modeling a system as a first-
order differential equation of the input (u), output (y), and state (x). If
the system is linear, the state and observation equations are respectively
represented by
x_ ¼Ax+Bu
(1.1)
y5Cx+Du
whereA,B,C,Darethematrixes.ItshouldbenotedthatD50formostof
the cases because it is the feedthrough term. If the system is nonlinear, the
state and observation equations are respectively represented by
x_ ¼fðx,uÞ
(1.2)
y5gðx,uÞ
Here, one simple example is shown. The model of mass, damper, and
spring is considered and illustrated in Fig. 1.1. Let x be the state, m be
the mass, d be the damping coefficient, k be the spring coefficient, and f
be the applied force. The equation of motion is then represented by
k
m f
d
x
Fig.1.1 Modelofmass,damper,andspring.
