scieee Science in your language
[en] (orig)

Recursive Forward Dynamics for Serial Kinematic Chains using Conformal Geometric Algebra

Abstract

The computation of the forward dynamics plays an important role in simulating the motion of interconnected rigid bodies while considering the physical properties and constraints of each part. The applications in graphics, animation, and robotics usually require fast computation, which leads to the usage of fast recursive algorithms. In this paper, we present a formulation of the recursive forward dynamics of serial kinematic chains that that is rooted in geometry, which allows coordinate-free view and geometrically meaningful interpretations of the involved quantities. The mathematical framework is called conformal geometric algebra (CGA) and it extends classical vector algebra by introducing a unified representation of a large array of geometric operations, transformations and mathematical objects, such as points, lines, and planes, in a rigorous yet intuitive manner. Hence, using CGA for the computation of the forward dynamics provides a unified mathematical framework that seamlessly integrates both the geometric and dynamic aspects of the system. We validate the computation numerically and provide an implementation of the results in an open-source library, making it immediately available in practice.

Read accessible full text

Recursive Forward Dynamics for Serial Kinematic Chains using Conformal Geometric Algebra

Author: Löw, Tobias; CALINON, Sylvain
Publisher: Zenodo
DOI: 10.5281/zenodo.17258592
Source: https://zenodo.org/records/17258592/files/80_Recursive_Forward_Dynamics_.pdf
Recu si e Fo wa d Dynamics o Se ial Kinema ic
Chains using Con o mal Geome ic Algeb a
Tobias Löw1,2[0000−0003−4001−2770] and Syl ain Calinon1,2[0000−0002−9036−6799]
1Idiap Resea ch Ins i u e, Ma igny Swi ze land.
{ obias.loew, syl ain.calinon}@idiap.ch
2EPFL, Lausanne, Swi ze land
Abs ac . The compu a ion o he o wa d dynamics plays an impo -
an ole in simula ing he mo ion o in e connec ed igid bodies while
conside ing he physical p ope ies and cons ain s o each pa . The
applica ions in g aphics, anima ion, and obo ics usually equi e as
compu a ion, which leads o he usage o as ecu si e algo i hms. In
his pape , we p esen a o mula ion o he ecu si e o wa d dynamics
o se ial kinema ic chains ha ha is oo ed in geome y, which al-
lows coo dina e- ee iew and geome ically meaning ul in e p e a ions
o he in ol ed quan i ies. The ma hema ical amewo k is called con o -
mal geome ic algeb a (CGA) and i ex ends classical ec o algeb a by
in oduc ing a uni ied ep esen a ion o a la ge a ay o geome ic ope -
a ions, ans o ma ions and ma hema ical objec s, such as poin s, lines,
and planes, in a igo ous ye in ui i e manne . Hence, using CGA o he
compu a ion o he o wa d dynamcis p o ides a uni ied ma hema ical
amewo k ha seamlessly in eg a es bo h he geome ic and dynamic
aspec s o he sys em. We alida e he compu a ion nume ically and p o-
ide an implemen a ion o he esul s in an open-sou ce lib a y, making
i immedia ely a ailable in p ac ice.
Keywo ds: Geome ic Algeb a, Dynamics
1 In oduc ion
Simula ion is an in eg al ool o obo ic enginee ing since i allows o he sa e
es ing and e alua ion o algo i hms. Simula ing he mo ion o obo s om o ces
using hei dynamics is u ilized in many di e en ields in obo ics and i is an
impo an pa in he de elopmen o new algo i hms o obo ics. The p oblem
o mapping o ces o mo ion is sol ed by compu ing he o wa d dynamics o
he obo , i.e. mapping he applied join o ques o join accele a ions o a
gi en join posi ion and eloci y. Consequen ly, he e a e many o - he-shel obo
dynamics simula o s a ailable, such as Raisim [1], Isaac Sim [2], Gazebo [3],
Mujoco [4], Bulle [5] o Coppelia Sim [6]. Fu he mo e, he e a e se e al so wa e
lib a ies ha , while no being ull simula ion engines, allow o he e icien
compu a ion o he obo dynamics, e.g. Pinocchio [7], KDL [8] and RBDL [9].
2 Tobias Löw and Syl ain Calinon
The e a e many applica ions whe e he o wa d simula ion o obo ic sys ems
has become an in eg al pa o algo i hm. O en hese o wa d simula ions a e un
in pa allel o ob ain ajec o y ollou s, which equi es e y e icien algo i hms
o he o wa d dynamics, such as in s ochas ic [10] and di e en ial dynamic
p og amming [11] op imal con ol. Sampling-based model p edic i e con ol, ha
is buil upon g adien - ee op imize s such as model p edic i e pa h in eg al
con ol [12], also hea ily le e ages pa allel ollou s p o ided by simula o s [13].
Rolling ou he sys em dynamics is also necessa y o dynamic p og amming
app oaches, including model p edic i e con ol [14], [15]. The same applies o
mo ion planning whe e he dynamics a e simula ed o wa d in ime o de e mine
he e ec s o he planned con ol command [16]. Apa om obo ics, he o wa d
dynamics also play an impo an ole in physics-based anima ion whe e hey a e
used o high-quali y cha ac e anima ions [17].
Ano he aspec when de eloping obo ic algo i hms, apa om he model-
ing o he obo i sel , is he modeling o he asks and he en i onmen . He e,
an inc eased ocus has been pu on exploi ing he unde lying geome y o he
p oblems. The di e en app oaches include powe ul ma hema ical echniques
such as Riemannian mani olds and Lie g oups. They ha e been used especially
a he in e sec ion o lea ning and con ol. Va ious wo ks p esen ed app oaches
o ep esen ing obo skills using Riemannian mani olds and i was shown ha
di e en mani olds can be used o ha pu pose [18]. The mani old can be p ede-
ined, such as lea ning o ien a ions [19], and le e aged in a dic iona y o di e en
mani olds [20]. In [21], he au ho s p esen ed a amewo k o geome y awa e
manipulabili y lea ning, and hen acking he lea ned ajec o y o manipula-
bili y ellipsoids and ans e ing i o di e en sys ems. These wo ks used he
mani old o symme ic posi i e-de ini e ma ices o he skill ep esen a ion. The
unde lying Riemannian mani old can, howe e , also be lea ned o ob ain mo ion
skills and hen la e be used o eac i e mo ion gene a ion [22]. Riemannian
mo ion policies a e ano he geome ic amewo k ha eac i e con ol [23]. In
addi ion o hese geome ic lea ning and con ol me hods, Riemannian op imiza-
ion is also used o exploi geome ic s uc u es o sol ing he in e se kinema ics
p oblem based on dis ance geome y [24].
Apa om hese geome ic echniques, a ma hema ical amewo k called
geome ic algeb a is also gaining ac ion in obo ics esea ch. The s o y o
geome ic algeb a in enginee ing is he s o y o an algeb aic amewo k ha
g ea ly simpli ies well-known equa ions, he mos popula example being he
Maxwell equa ions, which educe o a single equa ion in geome ic algeb a [25].
In obo ics, geome ic algeb a has been used o di e en ial kinema ics [26]. In-
e se kinema ics also se es as a good example o he geome ic signi icance o
geome ic algeb a objec s, since he p oblem can be sol ed i e a i ely by using
he in e sec ion ope a ions o he geome ic p imi i es in he algeb a [27]. O he
applica ions o hese geome ic p imi i es a e ools o medical obo ics o p o-
ide geome ic in ui i e echniques o planning su gical pa hs [28]. The e also
al eady exis s a link o he p e iously men ioned use-cases o he o wa d dy-
namics simula ion and geome ic algeb a in he o m o modeling op imal con ol
Fo wa d Dynamics o Kinema ic Chains using Geome ic Algeb a 3
p oblems using geome ic p imi i es [29]. Albei , in ha wo k he dynamics we e
included a e sol ing he op imal con ol p oblem in he o m o Lag angian in-
e se dynamics in o de o ob ain o que commands. Ano he in e es ing way o
use he geome ic p imi i es is o objec manipula ion om s e eoscopic images
[30]. Ma hema ically, geome ic algeb a p esen s esh insigh s on sc ew heo y
[31], which is o en used in obo ics o exp ess kinema ic ela ionships.
Despi e his g owing popula i y o geome ic algeb a in he ield o obo ics,
he e a e s ill many algo i hms ha need o be de i ed in hei geome ic algeb a
e sion in o de o i o become a single ool o compu a ion in obo ics. One
o hem is he e icien compu a ion o he o wa d dynamics, which we a e
add essing in his pape . Hence, ou con ibu ions a e as ollows:
–we de ine an ine ia enso using elemen s om con o mal geome ic algeb a,
–we o mula e he a icula ed body ine ia in con o mal geome ic algeb a,
–we o mula e he ecu si e o wa d dynamics in con o mal geome ic algeb a.
–we implemen he p esen ed algo i hms in an open-sou ce lib a y ga o3 ha
we i s p esen ed in [29]
The es o he pape is o ganized as ollows: Sec ion 2 p esen s ela ed wo k,
Sec ion 3 in oduces he ma hema ical backg ound o geome ic algeb a and
obo dynamics, in Sec ion 4 we show ou o mula ion o he ine ia enso in
CGA, in Sec ion 5 we hen in oduce he ecu si e dynamics algo i hms in CGA,
inally in Sec ion 6 we discuss some ma hema ical implica ions.
2 Rela ed Wo k
This pape desc ibes he ecu si e compu a ion o he o wa d dynamics in con-
o mal geome ic algeb a. Since his is an impo an concep in obo ics, na u-
ally, he e ha e been been many algo i hms and implemen a ions ackling his
p oblem. Ma hema ically i can be sol ed by de i ing he Lag angian o mula-
ion o he obo dynamics and sol ing o he join accele a ions. This app oach,
howe e equi es he in e sion o he gene alized mass ma ix, which is usually
no he mos e icien way o compu a ion [32]. Since hese dynamics simula ions
a e equen ly used in applica ions ha equi e a high compu a ional e iciency,
such as con ol, i is impo an ha hese accele a ions a e compu ed as as
as possible. The ecu si e o mula ion o sol ing he o wa d dynamics p oblem
called he A icula ed Body Algo i hm (ABA) was i s p esen ed in Fea he -
s one’s seminal wo k [33] and has been shown o ha e complexi y O(n). Ou
algo i hms build on his wo k and we will explain in he ma hema ical discus-
sions how he app oaches di e , and whe e he ad an ages o CGA lie.
Resea che s ha e ealized be o e ha ma hema ical ools om geome y can
lead o simpli ied and uni ied ea men s o he obo dynamics. By ea ing
SE(3) as a Lie g oup and adop ing basic ideas om Riemannian geome y, a
geome ic a ian o he obo dynamics was de i ed in [34]. Tha wo k showed
3h ps://gi lab.com/ga o
4 Tobias Löw and Syl ain Calinon
ha by looking a he p oblem h ough he lens o geome y, a ious imp ac ical
no a ional con en ions can be a oided and ha he connec ions o di e en ial
geome y lead o easily ac o izable and di e en iable equa ions, making i e y
a ac i e o op imiza ion and op imal con ol. These ideas o exploi ing Lie
g oups and Lie algeb as we e ex ended u he o show he na u al eme gence o
a ma ix ac o iza ion o he mass ma ix and i s in e se [35], whe e a ecu si e
algo i hm is embedded wi hin i s s uc u e. Fu he mo e, he inhe en in a iance
w. . he e e ence ame allows o a bi a y ames in he kinema ic modeling
[36]. Ou o mula ion in CGA no only e ains his coo dina e in a iance, bu
also, as we will explain la e , ac ually uses a double co e ing g oup o SE(3) o
i s compu a ions and ha he algeb a con ains mo e classes o ans o ma ions
including non- igid ones.
The heo y o dual qua e nion algeb a p esen s ano he pa adigm o ap-
p oaching p oblems in obo ics om a geome ic pe spec i e. The e a e wo ks
ha a e desc ibing he obo dynamics using dual qua e nion algeb a, cu en ly
in he o m o New on-Eule ype dynamics, which a e less e icien o he com-
pu a ion o he accele a ions [37]. A no able insigh om ha wo k is, ha by
combining he geome ic pe spec i e om sc ew heo y, he ho ougness o Lie
algeb a and a simple algeb aic amewo k in he o m o dual qua e nion algeb a
leads o simpli ied and mo e gene al exp essions. This insigh seamlessly ans-
la es o geome ic algeb a due o hei hei common oo s in Cli o d algeb a.
Hence, he e a e also wo ks ha u ilize geome ic algeb a o de i e a New on-
Eule ype algo i hm o obo in e se dynamics and con ol o manipula o s [38]
and mul icop e s [39]. In con as o hose wo ks, we a e p esen ing a ecu si e
o wa d dynamics algo i hm ollowing Fea he s one’s o malism o he a icu-
la ed body algo i hm. In o de o gi e a comple e o e iew and o in eg a e ou
ine ia enso o mula ion in o he New on-Eule algo i hm, we a e also showing
he in e se dynamics. P e ious wo k in geome ic algeb a also looked a he in-
e ia enso and showed ha i was ully con ained wi hin he geome ic algeb a
G0,6,2[40]. Since his algeb a is compa a i ely la ge, ha wo k asked he ques-
ion ega ding he minimal algeb a con aining he adjoin ope a ion o SE(3).
While he p esen ed ine ia enso and ans o ma ions o i equi e mul iple
ope a ions, he p oposed app oach is ully con ained wi hin he algeb a.
3 Backg ound
In his sec ion we a e b ie ly in oducing obo dynamics and geome ic algeb a.
We will use he ollowing no a ion h oughou he pape : x o deno e scala s, x
o ec o s, X o ma ices, X o mul i ec o s and X o ma ices o mul i ec-
o s.
3.1 Robo Dynamics
The manipula o equa ion o compu ing he dynamics o he sys em is
M(q)¨q+C(q,˙q)˙q+g(q) = τ−τex ,(1)
Fo wa d Dynamics o Kinema ic Chains using Geome ic Algeb a 5
whe e M(q)is known as he ine ia o gene alized mass ma ix, C(q,˙q)is ep-
esen ing Co iolis/cen i ugal o ces, g(q)s ands o he g a i a ional o ces, τis
he ec o o join o ques and τex a e he ex e nal o ques. The p oblem o o -
wa d dynamics hen a ises when sol ing Equa ion (1) o he join accele a ions
¨q, i.e.
¨q=M−1(q)τ−τex −C(q,˙q)˙q−g(q).(2)
This ma ix algeb a app oach o sol ing he o wa d dynamics he e o e equi es
inding he in e se o he gene alized mass ma ix M−1(q), which can be compu-
a ionally demanding. Hence, he p e ious wo k on de i ing ecu si e algo i hms
o he o wa d dynamics showed how he in e se mass ma ix could be e icien ly
ac o ized, alle ia ing he need o ma ix in e sion. This concep ansla es o
he o mula ion o he o wa d dynamics in CGA, since we also don’ compu e
he mass ma ix o i s in e se explici ly.
3.2 Geome ic Algeb a
Geome ic algeb as, also known as Cli o d algeb as, a e ac ually a amily o alge-
b as ha can be ound as he quo ien algeb as o enso algeb as o e quad a ic
spaces. His o ically, geome ic algeb a is a uni ica ion o Hamil on’s qua e nion
algeb a and G assmann’s ex e io algeb a. I is a single algeb a o geome ic
easoning, alle ia ing he need o u ilizing mul iple algeb as o exp ess geome ic
ela ions. Geome ic algeb as a e de ined o ec o spaces Rp,q, o dimension
n=p+q+ , whe e p, q and a e he numbe o basis ec o s ha squa e o
1,-1 and 0, espec i ely.
The undamen al mul iplica ion ope a ion is called he geome ic p oduc
ab =a·b+a∧b,(3)
and is he sum o an inne ·and an ou e ∧p oduc . The ou e p oduc is
equi alen o he ex e io p oduc o G assman algeb a and is an ope a ion ha
spans subspaces. The e o e, unlike he ec o algeb a o Rp,q, , he associa ed
geome ic algeb a Gp,q, uses subspaces o he associa ed ec o space as ele-
men s o compu a ions. The basic elemen s, called blades, a e ound as he ou e
p oduc o klinea ly independen ec o s, which leads o 2n= 2p+q+ blades
o a geome ic algeb a. The linea combina ion o basis blades is hen called a
mul i ec o . The numbe kis hen called g ade o he mul i ec o .
The compu a ion wi h subspaces leads o nullspace ep esen a ions o ge-
ome ic p imi es w. . ei he he inne o he ou e p oduc . In CGA, hese
p imi i es include poin s, lines, planes, ci cles and sphe es. Since hey a e no
wi hin he scope o his pape , we e e o [41] o hei de in ion and cons uc ion
and o [29] o hei usage in op imal con ol applica ions in obo manipula ion.
In his pape , we a e using CGA o o mula e he o wa d dynamics, which
is G4,1. As i has ou basis ec o s ha squa e o 1 and one ha squa es o -1
i is clea ly a pseudo-Euclidean space, in ac i has a Minkowski signa u e. I
can be unde s ood as ex ending he Euclidean space R3wi h a Minkowski plane

6 Tobias Löw and Syl ain Calinon
R1,1, i.e. R4,1=R3⊕R1,1. This leads o se e al ans o ma ions beyond he igid
body ans o ma ions p esen ed in he ollowing sec ion. We will discuss hose
ans o ma ions la e in Sec ion 6.4.
Rigid Body T ans o ma ions Rigid body ans o ma ions in CGA a e achie ed
using a ep esen a ion o he Lie g oup Spin(3)⋉R3called mo o s, which is a dou-
ble co e o he ma ix Lie g oup o igid body ans o ma ions in 3-dimensional
Euclidean space SE(3), i.e. he e exis s a su jec i e wo- o-one homomo phism
om Spin(3) ⋉ R3 o SE(3). The associa ed Lie algeb a o he mo o g oup is
a bi ec o algeb a and is connec ed o he mo o mani old by he exponen ial
map and i s in e se, he loga i hmic map. The bi ec o s o he Lie algeb a a e
essen ially (dual) lines in CGA, i.e. hey de ine he sc ew axis o he mo o .
Mo o s a e also isomo phic o dual qua e nions. The di e ence be ween hem is
ha CGA is a la ge algeb a which in oduces mo e geome ic p imi i es and
alle ia es he need o special adjoin ope a ions o ans o m hem.
Mo o s can be applied o a bi a y mul i ec o s in he algeb a by using a
sandwich p oduc ope a ion (simila o how qua e nions o a e ec o s)
Y=MX
M, (4)
whe e
Ms ands o he e e se o a mo o , which can hough o as being
simila o a conjuga e qua e nion. This ope a ion is g ade p ese ing, i.e. i
lea es he numbe o kbasis ec o s in ou e p oduc ep eseen a ion unchanged
and hus does no change wha he mul i ec o s ep esen . He ein lies one o he
ad an ages o CGA, as i ex ends linea ans o ma ions na u ally om ec o s
o he en i e algeb a, i.e. mo o s can be applied o all mul i ec o s in a uni o m
manne , hey a e a mo e gene al ep esen a ion o igid body ans o ma ions
and can also be used o ans o m all geome ic p imi i es ha a e pa o he
algeb a and no jus ec o s. This ex ension is called ou e mo phism.
Mo o s a e used o ep esen he o wa d kinema ics o a se ial kinema ic
chain, i.e. he mo o M(q), gi en he con igu a ion q, can be compu ed wi h
M(q) =
N
Y
j=1
Mj(qj).(5)
The mo o s Mj(qj)a e he cu en join mo o s o he j- h join gi en he join
posi ion. They a e ound as
Mj(qj) = exp (qjBj),(6)
whe e Bjis he bi ec o desc ibing he sc ew axis o he j- h join . In he ol-
lowing we will be using Mjins ead o Mj(qj) o a sho e no a ion.
Twis s and W enches U ilizing he e minology o sc ew heo y, he sc ews
ha ca y eloci y and o ce in o ma ion a e called wis s and w enches, espec-
i ely. In CGA, hey a e ep esen ed as bi ec o s. The wis s a e iden i ied wi h
Fo wa d Dynamics o Kinema ic Chains using Geome ic Algeb a 7
he bi ec o s ha gene a e igid body mo ions, i.e. he bi ec o ha esul om
he loga i hmic mapping o mo o s. Hence, hei space is ound as
V ∈ span{e23,e13,e12,e1∞,e2∞,e3∞},(7)
whe e he six objec s o ming a wis a e bi ec o s. The space o wis s alge-
b aically co esponds o he bi ec o dual lines ha o m he sc ew axes o
mo o s.
W enches on he o he hand, a e usually called cosc ews, meaning ha he e
is a ce ain duali y ela ionship be ween wis s and w enches. In ma ix Lie alge-
b a, howe e , his duali y is no di ec ly isible, since bo h wis s and w enches
a e simply 6-dimensional ec o s. In con o mal geome ic algeb a on he o he
hand, his duali y is explici ly ound ia mul iplica ion wi h he conjuga e pseu-
doscala Ic=Ie0[42], whe e Iis he s anda d pseudoscala o CGA, i.e.
I=e0123∞. Mul iplica ion o Icwi h a wis yields he space o w enches as
W ∈ span{e23,e13,e12,e01,e02,e03}.(8)
Using hese de ini ions, he inne p oduc o a wis Vand a w ench W
educes o he scala p oduc and calcula es he powe o he mo ion, i.e.
p=−V · W (9)
In Lie heo y, wis s a e elemen s o he Lie algeb a and w enches a e elemen s
o he dual Lie algeb a. As pe he abo e de ini ions his duali y ela ionship can
be di ec ly seen om he di e en bi ec o blades in he espec i e spaces. The
elemen s also allow o a di ec geome ical in e p e a ion: he linea eloci y
pa o wis s (i.e. e1∞,e2∞,e3∞) co esponds o a di ec ion bi ec o , whe eas
he o ce pa o w enches (i.e. e01,e02,e03) co esponds o a angen bi ec o .
This geome ic in e p e a ion u he cla i ies why wis s and w enches ans o m
di e en ly unde igid body ans o ma ions, i.e. why in ma ix Lie algeb a he
adjoin ma ix Ad ans o ms wis s and he dual adjoin ma ix Ad∗ ans o ms
w enches. Using con o mal geome ic algeb a, his dis inc ion is no necessa y,
since, by de ini ion o he algeb a, di ec ion and angen bi ec o s (i.e. linea
eloci ies and o ces) mul iply di e en ly using he geome ic p oduc . Hence, a
mo o can be used o ans o m bo h wis s and w enches acco ding o Equa ion
(4) which means i simul aneously ep esen s he adjoin and he dual adjoin
ope a ion.
Simila ly, a uni ied exp ession o he Lie b acke can be ound in con o mal
geome ic algeb a. The Lie b acke is a linea mapping be ween elemen s o he
Lie algeb a. Fo wis s ac ing on wis s o w enches his mapping can be ound
as
V′=V1× V2and W′=V × W,(10)
whe e ×is he commu a o p oduc ha is de ined as
X×Y=1
2(XY −Y X).(11)
8 Tobias Löw and Syl ain Calinon
The implica ions o hese de ini ions a e e y in e es ing, since CGA simul-
aneously cla i ies he duali y ela ionship o wis s and w enches, by emo ing
he ambigui y o wha a 6-dimensional ec o ep esen s h ough an algeb aically
de e mined di e ence. Fu he mo e, i also uni ies hei ea men by ha ing he
same adjoin ope a ions.
4 Ine ia Tenso
In his sec ion, we a e p esen ing ou o mula ion o he gene al ine ia enso in
CGA. This o mula ion is a di ec ex ension o he de ini ion o he o a ional
ine ia enso in dual qua e nion algeb a [37]. The o a ional pa can be iden-
i ied in con o mal geome ic algeb a wi h he bi ec o blades e23,e13,e12. We
add he bi ec o blades e01,e02,e03, such ha a gene al ine ia elemen becomes
I ∈ span{e23,e13,e12,e01,e02,e03}.(12)
No e ha an ine ia elemen uses he same basis blades as a w ench ha was
de ined in Equa ion (8). This ollows om he ac ha we wan he inne p oduc
o an ine ia elemen and wis s o be he scala p oduc in o de o compu e
he di e en componen s o he esul ing w enches.
Hence, he ine ia enso Iconsis s o six bi ec o - alued ine ia elemen s,
one o each componen o he esul ing w ench. We de ine he geome ic algeb a
ine ia enso as he union o all elemen s belonging o he blade index lis II,
i.e.
I={Iek}ek∈II,(13)
whe e he uppe blade index indica es which componen o he esul ing w ench
i is esponsible o . In acco dance wi h he de ini ion o a w ench, hese blade
indices he e o e a e
II={e23,e13,e12,e01,e02,e03}.(14)
4.1 Linea Mapping om Twis o W ench
The ine ia enso is a linea ope a o ha ac s on wis s and p oduces w enches.
This linea map can be ound as he sum o he inne p oduc s o each ine ia
elemen wi h he gi en wis , i.e.
W=I[V] = −X
ek∈II
(Iek· V)ek.(15)
A his poin we would like o men ion ha we gene ally call he ma he-
ma ical objec s wis s and w enches acco ding o he de inion o he spaces in
Equa ions (7) and (8), espec i ely. The physical in e p e a ion, howe e , can o
cou se be a mapping ei he om eloci y o momen um o accele a ion o o ce.
In bo h cases, he ma hema ical bi ec o spaces a e he same, hence we ea i
using a uni ied e minology.
Fo wa d Dynamics o Kinema ic Chains using Geome ic Algeb a 9
4.2 Rigid Body T ans o ma ion o Ine ia Tenso
O en, i is equi ed o iew he ine ia enso om a ame ha is di e en
o he ine ial ame. Hence, i is necessa y o ha e a o mula ion o how an
ine ia enso beha es unde igid body ans o ma ions. These a e exp essed
using mo o s in CGA, hence, gi en a mo o M, we de ine he ans o ma ion o
an ine ia enso and deno e his ope a ion using he symbol ∗, i.e.
M∗I=(M X
ek∈II
⟨MIek
M⟩ejek!
M)ej∈II
,(16)
whe e he ope a o ⟨·⟩ejex ac s he ejblade componen o he enclosed exp es-
sion.
Wi h equa ions (15) and (16), we can ind he equi a iance ela ionship
MI[V]
M= (M∗I)hMV
Mi.(17)
Hence, ans o ming a w ench p oduced by applying an ine ia enso o a wis ,
gi es he same w ench as i s ans o ming he ine ia and he wis indi idually
and hen applying he ans o med ine ia enso o he ans o med wis .
4.3 Spa ial Ine ia
T adi ionally, he spa ial ine ia is a 6×6symme ic ma ix ha con ains he
in o ma ion abou he o a ional ine ia, he mass and he cen e o mass. We
show in his sec ion how o ob ain i in ou CGA o malism, in o de o gi e a
mo e de ailed explana ion o he in ol ed ope a ions.
Gi en an a bi a y igid body, we ha e i s ine ial pa ame e s as he mass m,
he mo o desc ibing he loca ion o he cen e o mass MCoM and he ine ia
ma ix I, la e is a symme ic posi i e-de ini e ma ix wi h he six independen
componen s ixx, iyy, izz, ixy, ixz, iyz. Thus, we ind he he gene al ine ia enso
Ias a unc ion o mand I, i.e. he elemen s o Ia e
Ie23 =ixxe23 −ixye13 +ixze12,Ie01 =me01,
Ie13 =−ixye23 +iyye13 −iyze12,Ie02 =me02,(18)
Ie12 =ixze23 −iyze13 +izze12,Ie03 =me03.
No e he sign change in ce ain elemen s o he ine ia, ha is due o he me ic
enso o CGA, which led o ou choice o basis bi ec o s o closely ma ch and
acili a e he implemen a ion. Then, we ind he spa ial ine ia enso ICoM by
ans o ming he ine ia enso Iusing he mo o desc ibing he loca ion o he
cen e o mass MCoM
ICoM =MCoM ∗I.(19)
16 Tobias Löw and Syl ain Calinon
h ee bi ec o s ca ying he o ce in o ma ion om he w enches o m he gen-
e a o s o hype bolic o a ions, he e, an in e es ing u u e esea ch di ec ions
would be o unco e he implica ions o his connec ion. The las bi ec o e0∞
is esponsible o dila ions, which means ha CGA also con ains he g oup o
di ec simila i ies Sim(3) as a subg oup. The ac ha CGA con ains non- igid
ans o ma ions could mean ha he p esen ed dynamics algo i hms could be
ex ended o mo e complex dynamics such as so obo ics o o model de o mable
objec s.
7 Conclusion
In his pape , we p esen ed he o mula ion o he ecu si e o wa d dynamics
and he spa ial and a icula ed body ine ia in CGA. Al hough he p esen ed
esul s in hei cu en s age o esea ch a e o heo e ical na u e, we ha e shown
hei alidi y in simula ion expe imen s. Addi ionally, he algo i hms a e imple-
men ed in an open-sou ce lib a y, making hem eady o be used in p ac ical
applica ions. Fu u e wo k on his lib a y now includes add essing he p oblem
o con ac modeling in geome ic algb a, since his would be he nex s ep in o -
de o p o ide ull simula ion capabili ies wi h geome ic algeb a. Howe e , we
wan o poin ou ha by including he o wa d dynamics, geome ic algeb a, in
p inciple, is now capable o eplacing adi ional ec o /ma ix based lib a ies
o compu ing he kinema ics and dynamics o se ial kinema ic chains, since all
equi ed algo i hms a e now a ailable. I , howe e , o e s many addi ional ools
o geome ically modeling a ious p oblems in obo ics.
The o mula ion o CGA o e s a uni ied iew on sc ews, i allows o he
coo dina e in a ian o mula ions o Lie g oups and has compu a ional and ep-
esen a ional ad an ages. Thus i no only uni ies se e al amewo ks in o one
algeb a, bu also, ia he di ec connec ion o he pin g oup as a double co e
o he o hogonal g oup, i acili a es he geome ic in e p e a ion o con o mal
mappings compa ed o ma ix algeb a and makes hem compu a ionally mo e
e icien .
The cu en esea ch o cou se is limi ed by i s implemen a ion and he e-
sul ing lowe compu a ional pe o mance compa ed o o he lib a ies. Fu u e
esea ch on using CGA can he e o e ha e se e al di ec ions. Fi s , i should
be explo ed how he di e en possible implemen a ions o geome ic algeb a a -
ec he pe o mance o he algo i hms and o imp o e he implemen a ion in
gene al. And second, he ma hema ical backg ound o geome ic algeb a o e s
se e al di ec ions o possible ex ensions and applica ions o o he a eas such as
de o mable objec s and so obo ics. Fo his eason, he con ibu ions should
be seen om a ma hema ical pe spec i e, opening doo s o new po en ial e-
sea ch di ec ions and unco e ing ma hema ical and geome ic connec ions ha
a e hidden in o he amewo ks.

Fo wa d Dynamics o Kinema ic Chains using Geome ic Algeb a 17
Re e ences
1. Hwangbo, J., Lee, J. & Hu e , M. Pe -Con ac I e a ion Me hod o Sol ing
Con ac Dynamics. IEEE Robo . Au om. Le . 3, 895–902 (Ap . 2018).
2. Liang, J. e al. GPU-Accele a ed Robo ic Simula ion o Dis ibu ed Rein o ce-
men Lea ning h p://a xi .o g/abs/1810.05762 (2024). P e-published.
3. Koenig, N. & Howa d, A. Design and Use Pa adigms o Gazebo, an Open-Sou ce
Mul i-Robo Simula o in 2004 IEEE/RSJ In e na ional Con e ence on In elli-
gen Robo s and Sys ems (IROS) (IEEE Ca . No.04CH37566) 2004 IEEE/RSJ
In e na ional Con e ence on In elligen Robo s and Sys ems (IROS) (IEEE Ca .
No.04CH37566). 3(IEEE, Sendai, Japan, 2004), 2149–2154.
4. Todo o , E., E ez, T. & Tassa, Y. MuJoCo: A Physics Engine o Model-Based
Con ol in 2012 IEEE/RSJ In e na ional Con e ence on In elligen Robo s and
Sys ems 2012 IEEE/RSJ In e na ional Con e ence on In elligen Robo s and Sys-
ems (Oc . 2012), 5026–5033.
5. Coumans, E. & Bai, Y. PyBulle , a Py hon Module o Physics Simula ion o
Games, Robo ics and Machine Lea ning 2016–2021.
6. V-REP: A Ve sa ile and Scalable Robo Simula ion F amewo k | IEEE Con e ence
Publica ion | IEEE Xplo e h ps://ieeexplo e.ieee.o g/documen /6696520
(2024).
7. Ca pen ie , J. e al. The Pinocchio C++ Lib a y : A Fas and Flexible Imple-
men a ion o Rigid Body Dynamics Algo i hms and Thei Analy ical De i a i es
in 2019 IEEE/SICE In e na ional Symposium on Sys em In eg a ion (SII) 2019
IEEE/SICE In e na ional Symposium on Sys em In eg a ion (SII) (IEEE, Pa is,
F ance, Jan. 2019), 614–619.
8. Smi s, R. KDL: Kinema ics and Dynamics Lib a y h p://www.o ocos.o g/kdl.
9. Felis, M. L. RBDL: An E icien Rigid-Body Dynamics Lib a y Using Recu si e
Algo i hms. Au on Robo 41, 495–511 (Feb. 1, 2017).
10. Ca ius, J., Ran l, R., Fa shidian, F. & Hu e , M. Cons ained S ochas ic Op i-
mal Con ol wi h Lea ned Impo ance Sampling: A Pa h In eg al App oach. The
In e na ional Jou nal o Robo ics Resea ch 41, 189–209 (Feb. 2022).
11. Mas alli, C. e al. C ocoddyl: An E icien and Ve sa ile F amewo k o Mul i-
Con ac Op imal Con ol h p : / / a xi . o g / abs / 1909 . 04947 (2022). P e-
published.
12. Williams, G., Ald ich, A. & Theodo ou, E. A. Model P edic i e Pa h In eg al
Con ol: F om Theo y o Pa allel Compu a ion. Jou nal o Guidance, Con ol,
and Dynamics 40, 344–357 (Feb. 2017).
13. Pezza o, C. e al. Sampling-Based Model P edic i e Con ol Le e aging Pa al-
lelizable Physics Simula ions h p://a xi .o g/abs/2307.09105 (2024). P e-
published.
14. Sleiman, J.-P., Fa shidian, F. & Hu e , M. Cons ain Handling in Con inuous-
Time DDP-Based Model P edic i e Con ol in 2021 IEEE In e na ional Con e -
ence on Robo ics and Au oma ion (ICRA) 2021 IEEE In e na ional Con e ence
on Robo ics and Au oma ion (ICRA) (IEEE, Xi’an, China, May 30, 2021), 8209–
8215.
15. Bjelonic, M. e al. O line Mo ion Lib a ies and Online MPC o Ad anced Mo-
bili y Skills. The In e na ional Jou nal o Robo ics Resea ch, 22.
16. Plaku, E., Ka aki, L. E. & Va di, M. Y. Mo ion Planning Wi h Dynamics by a
Syne gis ic Combina ion o Laye s o Planning. IEEE T ans. Robo . 26, 469–482
(June 2010).
18 Tobias Löw and Syl ain Calinon
17. Liu, L., Yin, K., an de Panne, M., Shao, T. & Xu, W. Sampling-Based Con ac -
ich Mo ion Con ol.
18. Calinon, S. Gaussians on Riemannian Mani olds: Applica ions o Robo Lea ning
and Adap i e Con ol. IEEE Robo ics Au oma ion Magazine 27, 33–45 (June
2020).
19. Sa e iano, M., Abu-Dakka, F. J. & Ky ki, V. Lea ning S able Robo ic Skills on
Riemannian Mani olds. Robo ics and Au onomous Sys ems 169, 104510 (No . 1,
2023).
20. Ti, B., Razmjoo, A., Gao, Y., Zhao, J. & Calinon, S. A Geome ic Op imal Con-
ol App oach o Imi a ion and Gene aliza ion o Manipula ion Skills. Robo ics
and Au onomous Sys ems 164, 104413 (June 1, 2023).
21. Jaquie , N., Rozo, L., Caldwell, D. G. & Calinon, S. Geome y-Awa e Manipu-
labili y Lea ning, T acking, and T ans e . The In e na ional Jou nal o Robo ics
Resea ch 40, 624–650 (Feb. 2021).
22. Beik-Mohammadi, H., Haube g, S., A ani idis, G., Neumann, G. & Rozo, L. Re-
ac i e Mo ion Gene a ion on Lea ned Riemannian Mani olds. The In e na ional
Jou nal o Robo ics Resea ch, 02783649231193046 (Aug. 28, 2023).
23. Cheng, C.-A. e al. RMP low: A Geome ic F amewo k o Gene a ion o Mul i-
ask Mo ion Policies. IEEE T ansac ions on Au oma ion Science and Enginee ing
18, 968–987 (July 2021).
24. Ma ić, F. e al. Riemannian Op imiza ion o Dis ance-Geome ic In e se Kine-
ma ics. IEEE T ans. Robo . 38, 1703–1722 (June 2022).
25. Baylis, W. E. in Ablamowicz, R. e al. Lec u es on Cli o d (Geome ic) Alge-
b as and Applica ions (eds Abłamowicz, R. & Sobczyk, G.) 91–133 (Bi khäuse
Bos on, Bos on, MA, 2004).
26. Bay o-Co ochano, E. & Zamo a-Esqui el, J. Di e en ial and In e se Kinema ics
o Robo De ices Using Con o mal Geome ic Algeb a. Robo ica 25, 43–61 (Jan.
2007).
27. A is idou, A. & Lasenby, J. FABRIK: A Fas , I e a i e Sol e o he In e se
Kinema ics P oblem. G aphical Models 73, 243–260 (Sep . 2011).
28. Bay o-Co ochano, E., Ga za-Bu gos, A. M. & Del-Valle-Padilla, J. L. Geome ic
In ui i e Techniques o Human Machine In e ac ion in Medical Robo ics. In J
o Soc Robo ics 12, 91–112 (Jan. 2020).
29. Löw, T. & Calinon, S. Geome ic Algeb a o Op imal Con ol Wi h Applica ions
in Manipula ion Tasks. IEEE T ansac ions on Robo ics 39, 3586–3600 (2023).
30. Zamo a-Esqui el, J. & Bay o-Co ochano, E. Robo Objec Manipula ion Us-
ing S e eoscopic Vision and Con o mal Geome ic Algeb a. Applied Bionics and
Biomechanics 8, 411–428 (2011).
31. Dela osse, L. A New App oach o Sc ew Theo y Using Geome ic Algeb a.
32. Fea he s one, R. & O in, D. Robo Dynamics: Equa ions and Algo i hms in P o-
ceedings 2000 ICRA. Millennium Con e ence. IEEE In e na ional Con e ence
on Robo ics and Au oma ion. Symposia P oceedings (Ca . No.00CH37065) 2000
ICRA. IEEE In e na ional Con e ence on Robo ics and Au oma ion. 1(IEEE,
San F ancisco, CA, USA, 2000), 826–834.
33. Fea he s one, R. Robo Dynamics Algo i hms (Sp inge US, Bos on, MA, 1987).
34. Pa k, F., Bob ow, J. & Ploen, S. A Lie G oup Fo mula ion o Robo Dynamics.
The In e na ional Jou nal o Robo ics Resea ch 14, 609–618 (Dec. 1995).
35. Ploen, S. & Bob ow, J. An O(n) Geome ic Algo i hm o Manipula o Fo wa d
Dynamics in 1997 8 h In e na ional Con e ence on Ad anced Robo ics. P oceed-
Fo wa d Dynamics o Kinema ic Chains using Geome ic Algeb a 19
ings. ICAR’97 1997 8 h In e na ional Con e ence on Ad anced Robo ics. P o-
ceedings. ICAR’97 (July 1997), 563–568.
36. Mülle , A. Sc ew and Lie G oup Theo y in Mul ibody Dynamics: Recu si e Algo-
i hms and Equa ions o Mo ion o T ee-Topology Sys ems. Mul ibody Sys Dyn
42, 219–248 (Feb. 2018).
37. A onso Sil a, F. F., José Qui oz-Omaña, J. & Vilhena Ado no, B. Dynamics o
Mobile Manipula o s Using Dual Qua e nion Algeb a. Jou nal o Mechanisms
and Robo ics 14 (Sep . 14, 2022).
38. Bay o-Co ochano, E., Med ano-He mosillo, J., Osuna-González, G. & U ios egui-
Lego e a, U. New on–Eule Modeling and Hamil onians o Robo Con ol in he
Geome ic Algeb a. Robo ica 40, 4031–4055 (No . 2022).
39. A ellano-Mu o, C. A., Osuna-González, G., Cas illo-Toledo, B. & Bay o-Co ochano,
E. New on–Eule Modeling and Con ol o a Mul i-cop e Using Mo o Algeb a.
Ad . Appl. Cli o d Algeb as 30, 19 (Ap . 2020).
40. Selig, J. M. & Bay o-Co ochano, E. Rigid Body Dynamics Using Cli o d Alge-
b a. AACA 20, 141–154 (Ma . 2010).
41. Pe wass, C. Geome ic Algeb a wi h Applica ions in Enginee ing Geome y and
Compu ing 4(Sp inge , Be lin, 2009).
42. Hes enes, D. in Geome ic Algeb a Compu ing: In Enginee ing and Compu e
Science (eds Bay o-Co ochano, E. & Scheue mann, G.) 3–33 (Sp inge , London,
2010).
43. Fea he s one, R. Rigid Body Dynamics Algo i hms (Sp inge US, Bos on, MA,
2008).
44. Bay o-Co ochano, E. Robo Modeling and Con ol Using he Mo o Algeb a F ame-
wo k in 2019 12 h In e na ional Wo kshop on Robo Mo ion and Con ol (Ro-
MoCo) 2019 12 h In e na ional Wo kshop on Robo Mo ion and Con ol (Ro-
MoCo) (July 2019), 1–8.
45. Paszke, A. e al. PyTo ch: An Impe a i e S yle, High-Pe o mance Deep Lea ning
Lib a y.
46. Dan am, N. T. Robus and E icien Fo wa d, Di e en ial, and In e se Kinema ics
Using Dual Qua e nions. The In e na ional Jou nal o Robo ics Resea ch 40,
1087–1105 (Sep . 2021).
47. Fea he s one, R. A Beginne ’s Guide o 6-D Vec o s (Pa 1). IEEE Robo . Au-
oma . Mag. 17, 83–94 (Sep . 2010).
48. Vaz J ., J. & Rocha J ., R. da. An In oduc ion o Cli o d Algeb as and Spino s
Fi s edi ion. 242 pp. (Ox o d Uni e si y P ess, Ox o d, 2016).