# Embodiment and Computational Creativity

Christian Guckelsberger1–3,$\dagger$, Anna Kantosalo2,$\dagger$, Santiago
Negrete-Yankelevich4,$\ddagger$ Tapio Takala2,$\ddagger$
1Finnish Center for Artificial Intelligence
2Department of Computer Science, Aalto University, Espoo, Finland
3School of Electronic Engineering and Computer Science, Queen Mary University
of London, London, UK
4 Department of Information Technology, Metropolitan Autonomous University
(Cuajimalpa), Mexico City, Mexico
$\dagger$Contributed equally and share first authorship $\ddagger$Contributed
equally and share second authorship
<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>

###### Abstract

> We conjecture that creativity and the perception of creativity are, at least
> to some extent, shaped by embodiment. This makes embodiment highly relevant
> for computational creativity (CC) research, but existing research is scarce
> and the use of the concept highly ambiguous. We overcome this situation by
> means of a systematic review and a prescriptive analysis of publications at
> the International Conference on Computational Creativity. We adopt and
> extend an established typology of embodiment to resolve ambiguity through
> identifying and comparing different usages of the concept. We collect,
> contextualise and highlight opportunities and challenges in embracing
> embodiment in CC as a reference for research, and put forward important
> directions to further the embodied CC research programme.

## Introduction

Most researchers agree that _creativity_ and _intelligence_ are closely
intertwined (Kaufman and Plucker, 2011). Moreover, it is widely accepted that
intelligence is conditioned on _embodiment_ (Brooks, 1991; Clark, 1998); with
the words of Pfeifer and Scheier, “intelligence cannot merely exist in the
form of an abstract algorithm but requires a physical instantiation, a body”
(Pfeifer and Scheier, 2001, p. 649). Without insisting on physicality, we
conjecture that creativity, as a form of intelligent cognition, is also shaped
by embodiment.

This makes embodiment highly relevant across the entire continuum of
computational creativity (CC) research (Pérez y Pérez, 2018; Veale, Cardoso,
and Pérez y Pérez, 2019), from engineering artificial systems that can be
considered autonomously creative (Colton, 2008; Colton and Wiggins, 2012), to
understanding creativity in living beings through computational modelling and
simulation (Boden, 2003). Here, some of the most striking questions are if
reproducing human-like creativity by computational means is at all possible
without also reproducing human embodiment (Guckelsberger, Salge, and Colton,
2017; Valverde-Pérez and Negrete-Yankelevich, 2018), and how changes to a
system’s embodiment affect its potential creativity.

Unlike psychologists, whose human subjects share similar embodiments, CC
researchers can go beyond investigating the effect of embodiment on
_creativity per se_ and explore how people’s _perception of creativity_
(Colton, 2008; Colton, Pease, and Saunders, 2018) as exhibited by an
artificial system is affected by their own and the system’s embodiment. If
such an effect existed, embodiment would have to be considered an integral
factor in the evaluation of CC (Jordanous, 2012) to facilitate fairer
comparisons between computational systems and with human creativity. Moreover,
using this knowledge, researchers could tune a system’s embodiment to improve
the perception of its creativity, and, vice versa, how it perceives the
creativity of others.

Given these potential ramifications, it is surprising and alarming that
“embodiment” seems to have received little attention in CC research. One
potential reason for the apparent void is that the very concept is highly
ambiguous (Ziemke, 2003). Theories of embodied cognition – from minimal
accounts that “rule out anatomy and bodily movement as important” (Gallagher,
2011), to radical approaches that understand cognition as inextricably bound
to bodily processes – conceptualise embodiment differently. Moreover, embodied
cognition is closely associated with other, popular extra-cranial and extra-
bodily theories of cognition, in particular theories of enactive, embedded and
extended cognition. Due to their proximity and interdependence, they are
frequently grouped together into the complex of “4E cognition”.

Crucially though, there appears to be very little awareness of this ambiguity
and complexity within CC research. At present, it is perfectly imaginable that
two researchers excitedly referred to the “embodiment” of their respective
system without noticing that they are talking about entirely different things.
We consider this a major problem, given that the various types of embodiment
likely have radically different effects on (the perception of) creativity. We
argue that the advancement of the field through concerted investigations
requires the use of common definitions of embodiment.

The first goal of this paper is to counteract this ambiguity and provide a
rich overview of what some authors have already coined “embodied computational
creativity (CC)” (Saunders and Gemeinboeck, 2014; Guckelsberger, Salge, and
Colton, 2017; Colton, Pease, and Saunders, 2018) research. Based on a
systematic review of related work at the International Conference on
Computational Creativity (ICCC) as the prime and domain-agnostic venue of CC
research, we answer the following research questions:

RQ1:

What types of embodiment have been embraced in CC research, and how has the
usage evolved over time?

RQ2:

Why did CC researchers embrace these embodiment types in their work, and what
challenges did they face?

RQ3:

What does CC research reveal about the relationship of embodiment and (the
perception of) creativity?

To counteract ambiguity in the usage of the embodiment concept, we extend and
apply a well established typology of embodiment informed by cognitive science
to assess the specific types addressed in each relevant contribution. By
making transparent which types of embodiment have been embraced, and by
highlighting our challenges in assessing them, we want to provide a frame of
reference for researchers to adequately and unambiguously address questions of
embodiment in their work. Our insights moreover allow us to to provide
recommendations for an embodied CC research programme – the second goal of
this paper.

## Types of Embodiment

Much research focuses on distinguishing theories of embodied cognition (e.g.
Gallagher, 2011), but comparisons of the underlying and varying
conceptualisations of embodiment are rare. To disambiguate different uses of
the embodiment concept in CC, we adopt and extend the well-established
typology by Ziemke (2003), who distinguishes six types of embodiment informed
by research in cognitive science and robotics. We introduce three additions to
this typology (one additional type, two additions to existing types) based on
more recent insights, and highlight them in _italics_ below.

structural coupling, characterising systems that can perturbate, and, vice
versa, be perturbated by their surrounding environment (Varela, Rosch, and
Thompson, 1991). Such perturbations facilitate a minimal interaction between
the system and environment, in which each has the potential to affect the
other’s state (Quick et al., 1999).

historical, characterising systems whose present state is the result of a
history of structural couplings, developed through interactions with the
environment over time (Varela, Rosch, and Thompson, 1991; Ziemke, 1999).

virtual, _characterising simulated systems embedded in and distinguished from
a simulated environment. The virtual body can act on the environment and vice
versa._

physical, characterising systems with a physical body (Brooks, 1990; Pfeifer
and Scheier, 2001) that can interact with the environment by being subjected
to and by exercising physical force. Most prominently, robots are physically
embodied (Pfeifer, Iida, and Bongard, 2005).

organismoid, characterising _virtually_ or physically embodied systems with
the same or a similar shape and sensorimotor equipment as living organisms,
e.g. animals. We consider humanoid embodiment as approximations of the human
body a subset of organismoid embodiment.

organismic, applying to living _and artificial_ systems capable of
organisational closure, i.e. of maintaining their organisation and surrounding
boundary against internal and external perturbations by means of self-
producing processes (Von Uexküll, 1920; Maturana and Varela, 1987). A
prominent, minimal example is the living cell which, in a self-referential
process, maintains its organisation, including its membrane, against
perturbations from the surrounding environment (Agmon, Gates, and Beer, 2016).

We briefly justify our additions. We have complemented _physical_ with
_virtual embodiment_ , because AI researchers have successfully reproduced
(super-)human cognitive abilities in virtual agents, embedded in e.g. high
fidelity physics simulations (Lillicrap et al., 2015) or, often more coarse,
videogame worlds (Mnih et al., 2015). Applying AI techniques to virtual agents
in simulated worlds rather than to physically embodied systems allows for
scalability, incremental development, and rapid iteration, amongst other
advantages (Kiela et al., 2016). As a corollary, we have extended
_organismoid_ embodiment to _virtually embodied_ systems, e.g. in the form of
believable game characters with a human or animal-like appearance. We have
finally extended _organismic embodiment_ to artificial systems. Originally
restricted to the biochemical domain, this type required the capacity for
autopoiesis, i.e. self-production, to facilitate a radical form of autonomy.
Varela overcomes this limitation by introducing the concept of organisational
closure as “operational characterization of autonomy in general, living or
otherwise” (Varela, 1979). Froese and Ziemke (2009) advocate that
organisational closure can be realised by AI systems, and survey existing
examples. We can thus consider organismic embodiment in artificial systems,
which makes it relevant for CC research. Although organismic embodiment is
arguably the least well-established type, we include it for its presence in
existing CC theory (Saunders, 2012; Guckelsberger, Salge, and Colton, 2017),
its potential future implications for CC, and its central role in related
debates, e.g. on agency (Polani, Ikegami, and Biehl, 2016).

In our adaptation of Ziemke’s (2003) typology, we have dropped what e.g.
Dautenhahn (1997) and Barsalou et al. (2003) refer to as social embodiment,
because it denotes the use of different types of embodiment to facilitate
social interaction (Ziemke, 2003), and is thus orthogonal to, and not at the
same “atomic” level, as the other types. Metzinger (2014) has proposed a
distinction between 1st, 2nd and 3rd order embodiment based on a system’s
computational abilities, corresponding to (1) physical, reactive systems
without explicit computation, (2) systems that explicitly represent themselves
as embodied agents, and (3) systems that can consciously experience some of
these body representations. We disregard this typology as (i) it is not
derived from existing work on embodied cognition more generally and serve the
specific purpose of grounding (artificial) consciousness as one aspect of
cognition, and because (ii) it would only warrant little differentiation of
existing work; most CC systems presently fall into a gap between the 1st and
2nd type.

Crucially, the extended typology is only loosely hierarchical. _Historically_
as well as _physically_ and _virtually_ embodied systems are all _structurally
coupled_. _Physically_ and _virtually_ embodied systems in turn can, but do
not have to be _historically_ embodied. _Organismoid_ systems can be
_virtually_ or _physically_ embodied, but we reserve _organismic_ embodiment
to _physically_ embodied systems, as autonomy via organisational closure
relies on physical forces that pose a real threat to a system’s organisation
and boundary.

## Review Method

To investigate how embodiment has been discussed in CC research, we performed
a systematic literature review on the proceedings of the International
Conference on Computational Creativity (ICCC), the prime venue for CC
research, between its inception in 2010 and its latest edition in 2020. We
expect our findings to be representative, as ICCC gathers a wide audience of
CC researchers and practitioners, and welcomes contributions covering any
creative domain, creative practice and aspect of creative cognition
(Association for Computational Creativity, 2021). We constrained our review to
paper candidates that explicitly mention the words “embodiment”, “embodied”,
“disembodiment”, “disembodied”, “embody”, or “embodying”.

We acknowledge that our reliance on the explicit usage of the word may
overlook a large amount of potentially relevant papers. This particularly
concerns work on robotics, which often does not include explicit mentions of
the embodiment of the investigated systems. Simply including the term
“robotics” in our search however was impractical, as, for fairness, it would
have required to also include any other type of system characterised by the
remaining embodiment types. Given the inclusiveness of some types, the amount
of potentially related papers would likely go beyond what could reasonably be
reviewed in depth. Moreover, we believe that our present approach allows us to
identify intentional and thus more informative discussions of the relationship
between embodiment and (the perception of) creativity.

For our initial candidate paper selection, we divided the past ICCC
proceedings into pdf files, each containing one paper, short paper, demo
description, or other peer reviewed publication. We used a wildcard search in
Adobe Acrobat reader with the phrase “*embod*” and the hyphenated version
“*em-bod*”. In total we found 99 papers mentioning the word “embodiment”,
“embodied”, “disembodiment”, “disembodied”, “embody”, or “embodying”
explicitly, with a total of 491 matches to the search phrases.

We then reduced the candidate papers for our final, qualitative analysis in a
two-stage process. We first excluded papers which mentioned these words only
in the References section (7 papers), or as part of a general list of CC
related topics (2 papers). We secondly excluded papers that used these
keywords in a merely metaphorical way, such as suggesting that a specific
algorithm or system “embodies” certain values (51 papers). This left us with a
final pool of 40 papers for in-depth analysis. Figure 1 illustrates the
overall usage of the term over the years, thus partly answering RQ1.

Figure 1: Absolute annual numbers of ICCC papers using words derived from
‘embodiment’ in a (non-)metaphorical way. On average, 7.1% of papers use the
word ‘embodiment’ or its derivatives non-metaphorically, with the lowest
proportion (0%) in 2012 and the highest (11.8%) in 2017 and 2020. Overall the
term is used throughout the proceedings, but non-metaphorical usage has
increased over the years.

We assessed the embodiment of any system described in the remaining papers,
either introduced there or through reference to other work, based on Ziemke’s
(2003) extended typology. Since explicit definitions of embodiment were mostly
absent, this usually required us to look at the specific characteristics of
the system in question. To answer our research questions, we also gathered
notes on:

  * •

Which challenges did we encounter in assessing the embodiment described in the
paper?

  * •

Did we identify any embodiment types that were not yet present in our
typology? (RQ1)

  * •

Why have the authors embraced this particular type of embodiment, and what
challenges did they face? (RQ2)

  * •

What did the paper express about the relationship between embodiment and
creativity, or between embodiment and the perception of creativity? (RQ3)

Each of the remaining 40 papers was first assessed by one of the four
researchers participating in the effort. We then discussed the challenges
encountered, and cross-checked each paper with another colleague in the team.
Any potential disagreements were resolved in dedicated discussions. Another
four papers were removed during this step because the explicit mentions to
embodiment were cursory. This reduced the final number of papers to 36.

The answers to the questions presented above were analysed by building an
_affinity diagram_ , a _qualitative_ data analysis method used to group data
into emergent categories (Rogers, Sharp, and Preece, 2011, p. 286). We first
built individual affinity diagrams from the answers collected for each
question by grouping similar items together and labelling them. We then merged
categories between the different questions, yielding one large affinity
diagram as a connected view of different aspects of embodiment in CC. We
finally identified overarching themes within this structure.

|  |  |  |  | Embodiment Type
---|---|---|---|---|---
Authors | Year | Abbreviated Title |  Concrete System(s) |  Mult. Embodiments |  Struct. Coupling |  Historical |  Virtual |  Physical |  Organismoid |  Humanoid |  Organismic
de Melo & Gratch | 2010 | Evolving Expression of Emotions | — | — | ◐ | ○ | ◐ | ○ | ◐ | ◐ | ○
Saunders et al. | 2010 | Curious Whispers | ✓ | — | ● | ● | ○ | ● | ◐ | ○ | ○
Kirsh | 2011 | Creative Cognition in Choreography | — | — | ● | ● | ○ | ● | ● | ● | ●
Gemeinboeck & Saunders | 2013 | Creative Machine Performance: Robotic Art | ✓ | — | ● | ● | ○ | ● | ◐ | ○ | ○
Saunders, Chee & Gemeinboeck | 2013 | Evaluating Human-Robot Interaction | ✓ | — | ● | ● | ○ | ● | ◐ | ○ | ○
Schubert & Mombaur | 2013 | Motion Dynamics in Abstract Painting | ✓ | — | ● | ◐ | ○ | ● | ○ | ○ | ○
Schorlemmer et al. | 2014 | COINVENT: Concept Invention Theory | — | — | ○ | ○ | ○ | ○ | ○ | ○ | ○
Davis et al. | 2014 | Building Artistic Computer Colleagues | ✓ | — | ● | ○ | ○ | ◐ | ○ | ○ | ○
Jacob & Magerko | 2015 | Interaction-based Authoring | ✓ | — | ● | ● | ● | ○ | ● | ◐ | ○
Takala | 2015 | Preconceptual Creativity | ✓ | — | ● | ● | ● | ○ | ○ | ○ | ○
Carlson et al. | 2016 | Cochoreo: Generative Choreography | ✓ | — | ● | ○ | ● | ○ | ○ | ○ | ○
McCormack & d’Inverno | 2016 | Designing Improvisational Interfaces | ✓ | — | ● | ○ | ○ | ○ | ○ | ○ | ○
Crnkovic-Friis & Crnkovic-Friis | 2016 | Generative Choreography using Deep Learning | ✓ | — | ○ | ○ | ○ | ○ | ○ | ○ | ○
Guckelsberger et al. | 2016 | Supportive and Antagonistic Behaviour | ✓ | ✓ | ● | ● | ● | ● | ◐ | ○ | ○
Brown | 2016 | Understanding Musical Practices | ✓ | — | ● | ○ | ○ | ○ | ○ | ○ | ○
Augello et al. | 2017 | Creative Robot Dance With Variational Encoder | ✓ | — | ● | ● | ● | ○ | ● | ● | ○
Fitzgerald, Goel & Thomaz | 2017 | Human-Robot Co-Creativity: Task Transfer | ✓ | — | ● | ● | ○ | ● | ● | ● | ○
Guckelsberger, Salge & Colton | 2017 | Non-Anthropocentric Intentional Creative Agency | ✓ | ✓ | ● | ● | ○ | ● | ● | ● | ●
Singh et al. | 2017 | Unified Classification and Generation Networks | ✓ | — | ● | ○ | ● | ○ | ○ | ○ | ○
Colton, Pease & Saunders | 2018 | Issues of Authenticity in Creative Systems | ✓ | ✓ | ● | ● | ○ | ● | ◐ | ○ | ○
Hedblom et al. | 2018 | Under the Super-Suit: Conceptual Blending | ✓ | — | ○ | ○ | ○ | ○ | ○ | ○ | ○
Saunders & Gemeinboeck | 2018 | Performative Body Mapping for Robots | ✓ | ✓ | ◐ | ○ | ○ | ◐ | ◐ | ○ | ○
Wicke & Veale | 2018 | Interview With the Robot | ✓ | — | ● | ◐ | ○ | ● | ● | ● | ○
Jacob et al. | 2019 | Affordance-Based Generation of Object Variants | ✓ | — | ○ | ○ | ● | ○ | ○ | ○ | ○
Onate, Mendez & Gervas | 2019 | Elevator Conversations Based on Emotions | ✓ | — | ○ | ○ | ○ | ○ | ○ | ○ | ○
Veale, Wicke & Mildner | 2019 | Duets Ex Machina: “Double Acts” | ✓ | ✓ | ● | ○ | ○ | ● | ● | ● | ○
Loesel, Mirowski & Mathewson | 2020 | Do Digital Agents Do Dada? | ✓ | ✓ | ◐ | ○ | ● | ◐ | ◐ | ◐ | ◐
Alexandre | 2020 | Creativity Explained by Cognitive Neuroscience | ✓ | — | ● | ● | ○ | ● | ● | ● | ●
Chen et al. | 2020 | Breaking the Imitation Barrier | ✓ | — | ○ | ○ | ○ | ○ | ○ | ○ | ○
Ferguson et al. | 2020 | Automatic Similarity Detection in LEGO Ducks | ✓ | — | ○ | ○ | ○ | ○ | ○ | ○ | ○
Kantosalo et al. | 2020 | Modalities, Styles and Strategies | — | — | ● | ○ | ○ | ● | ○ | ○ | ○
Kantosalo & Takala | 2020 | Five C’s for Human–Computer Co-Creativity | ✓ | ✓ | ● | ○ | ○ | ● | ○ | ○ | ○
LaViers & Vidrin | 2020 | Can a Robot Do a Trust Fall? | — | — | ● | ○ | ○ | ● | ○ | ○ | ○
Savery, Zahray & Weinberg | 2020 | Shimon the Rapper: Robot Rap Battles | ✓ | — | ● | ◐ | ○ | ● | ● | ● | ○
Wallace et al. | 2020 | Movement Generation with Audio Features | ✓ | — | ○ | ○ | ○ | ○ | ○ | ○ | ○
Wicke & Veale | 2020 | Show, Don’t (Just) Tell | ✓ | — | ◐ | ○ | ○ | ● | ● | ● | ○

Table 1: Chronological overview of ICCC (2010-2020) papers mentioning the
concept of embodiment explicitly and non-metaphorically. The circles ○ ◐, and
● represent increasing degrees to which the described embodiment(s) match our
types. Organismoid embodiment entails humanoid embodiment, but we also
discriminate humanoid embodiment separately. For papers describing multiple
embodied systems, e.g. a robot and a human, individual embodiment types were
combined with a logic “or”. Papers introducing a “concrete system” have been
marked; other papers consider theory or abstract systems.

## Findings

We report our findings on the state-of-the-art of embodied CC in individual
subsections. We first detail the types of embodiment identified in existing
work, thus conclusively answering RQ1. We elaborate on our difficulties in
this process later in the discussion section, where we go beyond the present
findings and make recommendations for future research. Our review uncovered
that existing insights on the relationship of embodiment and (the perception
of) creativity are often tightly aligned with researchers’ motivations to
embrace a certain type of embodiment in their work. In the second part of our
findings, we consequently address RQ2 and RQ3 jointly through themes
corresponding to _opportunities_ and _challenges_ for embodied CC. We
distinguish each theme as a paragraph heading, point out sub-themes in bold,
and highlight which embodiment type it relies on in _italics_.

### Embodiment Types

Table 1 provides an overview of our final paper selection, together with our
assessment of the described embodiment. We found that _structural coupling_
and _physical embodiment_ are most common, each appearing in almost twenty
papers. _Historical_ , _virtual_ and _organismoid_ mentions appear quite
equally, each in about ten papers. _Organismic embodiment_ was only identified
in four papers; Guckelsberger, Salge, and Colton (2017) discuss it with
respect to machines, while the other three instances relate to humans, which
are organismically embodied by definition. Over the years, although there is
considerable variation, the annual number of papers grows from 1.6 on average
in the first half to 3.6 in the latter half of the decade, until there is a
sudden peak of 10 in 2020. Mentions of different types follow a similar trend,
except that _structural coupling_ gives way to the more specific, _physical
embodiment_ in recent years. Out of the 36 papers, 23 present a concrete,
embodied computational system, seven are at least partly theoretical or
appealing to the embodiment of humans. In another seven papers, we were unable
to identify one or multiple types of embodiment at all, indicated by a row of
empty circles in Table 1.

Our analysis highlights _virtual_ and _physical embodiment_ as strongest
differentiators of existing work. It moreover identified a chasm between
researchers poised to leverage _humanoid embodiment_ , and those rejecting it
for the benefits of other variations of _organismoid embodiment_.

Through our review, we identified one embodiment type that was not directly
present in our extension of Ziemke’s (2003) typology: Loesel, Mirowski, and
Mathewson (2020) introduce “cyborg embodiment”, which bridges between
_virtual_ , _organismoid (anthropomorphic)_ and _physical embodiment_. It was
demonstrated in their theatrical experiment “AI Improv”, where a chatbot
provides sentences to be articulated by a human actor, and receives new
prompts to react to from a backstage operator who monitors the on-stage
dialogue. The two people thus provide a split actuation and sensing interface
to the artificial system, allowing for it to be embedded in the physical
environment of the stage.

### Opportunities of Embodied CC

Our analysis identified nine opportunities of embracing embodiment in CC. We
can distinguish two sub-groups, based on how the themes relate to the concept
of interaction. All types of embodiment distinguished in our typology assume
at least a minimal form of interaction between an agent and its environment in
the form of _structural coupling_. The first four themes concern how this
embodiment-induced split provides opportunities for modelling a specific
creative domain, outsourcing computation, letting creativity emerge and
stimulating it. The remaining five themes operate on a stronger notion of
interaction between agents, i.e. where the embodied agent’s environment
comprises interaction partners. In this group, embodiment is considered a
means to model co-creativity, ground meaning, facilitate more natural
interaction with people, support identification and empathy with the
computational agent, and increase the CC system’s creative intentionality and
autonomy.

#### Domain Necessity

_Any type of embodiment_ presents the opportunity to model creative processes
that unfold between an agent and their environment. Some creative domains may
necessitate this split more than others in order to comprehensively model the
creative processes within. Dance choreography represents a prime example
(Augello et al., 2017; Carlson et al., 2016), but embodiment has also been
embraced in e.g. music (Schorlemmer et al., 2014) and painting (Schubert and
Mombaur, 2013; Singh et al., 2017) to model creative processes that rely on
sensorimotor feedback between an agent and their surroundings.

#### Outsourcing Computation

Embodied CC has adopted several premises of embodied AI more generally,
notably the use of _physical embodiment_ to outsource computation into the
physical world. Saunders, Chee, and Gemeinboeck (2013) note that physical
embodiment allows artificial agents “to take advantage of properties of the
physical environment that would be difficult or impossible to simulate
computationally” (paraphrasing Brooks, 1990; see also Gemeinboeck and
Saunders, 2013). They thus relate to one of the most prominently articulated
benefits of embodied AI: the use of the world “as its own model” (Brooks,
1991).

#### Emergent Creativity

While the outsourcing of computation seems a mere engineering benefit at
first, it has major implications for the creativity that a _physically
embodied_ system can potentially exhibit. In particular, it allows to realise
the very premise of systems theories of creativity (Csikszentmihalyi, 1988),
the emergence of creative behaviour through an agent’s interaction with their
environment, including other agents: “embodiment provides opportunities for
agents to experience the emergence of effects beyond the computational limits
that they must work within” (Saunders, Chee, and Gemeinboeck, 2013). In the
art installation _Zwischenräume_ , the robots’ creative agency “is not
predetermined but evolves based on what happens in the environment they
examine and manipulate” (Gemeinboeck and Saunders, 2013). This emergence
benefits from a controller that is not pre-coded but sensitive to an agent’s
changing embodiment. Several authors (e.g. Saunders et al., 2010; Saunders,
Chee, and Gemeinboeck, 2013; Guckelsberger et al., 2016; Guckelsberger, Salge,
and Colton, 2017) highlight the use of computational intrinsic motivation to
this end. A system with a suitable controller can leverage its embodiment to
expand its behavioural range beyond what can be anticipated by the system
designer, realising novelty as core criterion for creativity (Rhodes, 1961).

#### Stimulation of Creativity

As a specific case of emergent creative behaviour, several researchers argue
that constraints imposed through embodiment can stimulate creativity. Drawing
on Pickering (2005), Saunders et al. highlight that the “world offers
opportunities, as well as presenting constraints: human creativity has evolved
to exploit the former and overcome the latter, and in doing both, the
structure of creative processes emerge” (Saunders et al., 2010). Guckelsberger
et al. (2016) argue in an artistic context, drawing on different embodied CC
systems and a thought-experiment on the _physically embodied_ robot society
_Curious Whispers_ (Saunders et al., 2010), that overcoming embodiment-related
constraints in an environment can necessitate and – given a suitable agent
controller – yield creativity. Takala (2015) demonstrates this on a simulated
robotic arm capable of inventing new and useful movements when encountering
obstacles. This also highlights that creative action is possible without
creative reasoning, a distinction which is later picked up by Fitzgerald,
Goel, and Thomaz (2017). By utilising _virtual embodiment_ , Takala
demonstrates that the effect of embodiment constraints on creativity can be
investigated without physical embodiment. However, the use of physical
embodiment can better alleviate doubts about the emergent behaviour being
truly novel, and not engineered a priori into a simulated environment.

#### Co-Creativity

Many of the analysed papers express a focus on stronger forms of interaction
between agents. Two particular modes of interaction are given by human-machine
and machine-machine co-creativity (Saunders and Bown, 2015; Kantosalo and
Toivonen, 2016). This focus can be explained with the observation that
embodiment is a prerequisite for co-creativity. Guckelsberger et al. (2016)
highlight that “co-creative and social creativity systems are only meaningful
if each agent has a different perspective on a shared world, allowing them to
complement each other, and for creativity to emerge from their interaction”.
The necessary separation of agent and environment is crucially facilitated by
_any type of embodiment_. This allows for the attribution of embodiment to a
given system, based on the systemic nature of the system alone. The next three
themes represent additional lenses on human-machine co-creativity.

#### Grounding Meaning

Another central premise of more radical theories of embodied cognition,
bordering to enactivism (Varela, Thompson, and Rosch, 2017), is that _physical
embodiment_ can overcome symbolic representations and ground meaning in
sensorimotor interaction (e.g. Dreyfus, 1992). Colton, Pease, and Saunders
(2018) stress that this allows re-representing creative domains in action. As
an example, they refer to the Marimba playing robot _Shimon_ (Hoffman and
Weinberg, 2010) which represents music as choreography of physical gestures.
They also emphasise the grounding of machine “life experiences” as an
important factor in increasing the perception of authenticity in CC systems.
Related, Wicke, Veale and Mildner exploit robot gestures to provide the
illusion of grounding computer-generated stories (Wicke and Veale, 2018, 2020;
Veale, Wicke, and Mildner, 2019), thus leveraging embodiment to affect the
perception of their robot’s creativity.

#### Natural Interaction

Saunders et al. (2010) are first to stress that _physical embodiment_ allows
for CC systems to be embedded in rich social and cultural environments. This
enables “computational agents to be creative in environments that humans can
intuitively understand” (Saunders, Chee, and Gemeinboeck, 2013). Robotic art
installations are highlighted as one means to “gain access to shared social
spaces with other creative agents, e.g., audience members” (Gemeinboeck and
Saunders, 2013). Existing research often stresses that situating CC systems in
physical space realises more natural interaction by established means, and can
unleash new modes of interaction. This is explained by _physical embodiment_
affording tight feedback loops (Wicke and Veale, 2018) and providing stronger
cues to the human interaction partners (Saunders, Chee, and Gemeinboeck,
2013). In some instances, this interaction is constrained to a few
invididuals, e.g. when situating a robot on stage to interact with musicians
(Savery, Zahray, and Weinberg, 2020) or actors (Loesel, Mirowski, and
Mathewson, 2020). In their art installation _Zwischenräume_ , Gemeinboeck and
Saunders in contrast open the interaction to a wider audience in an exhibition
space, permitting “the development of significantly new modes of interaction”
and “engaging a broad audience in the questions raised by models of artificial
creative systems” (Gemeinboeck and Saunders, 2013). Within natural interaction
spaces, _physical embodiment_ can “improve the relationship between humans and
AI, inspiring humans in new creative ways” (Savery, Zahray, and Weinberg,
2020), e.g. in partnering a human musician with _Shimon’s_ reincarnation as
rapper.

#### Identification & Empathy

Within the overarching theme of agent interaction, researchers embraced
_physical_ , and in particular _organismoid_ and _humanoid embodiment_ to
facilitate and improve communication and, consequently, to afford
identification, empathy and affect between human and robot, or within a
society of robots. Gemeinboeck and Saunders highlight embodied action in their
installation _Zwischenräume_ as a means for communication; it takes the form
of robots creating noises with a hammer which members of an exhibition
audience and other robots can perceive and react to. They moreover stress from
an enactivist perspective that the robots’ actions provide “a window on the
agents’ viewpoint” (Gemeinboeck and Saunders, 2013), thus possibly
facilitating more introspection. Wicke and Veale refer to work outside CC to
emphasise that, “when identification with the [story]teller is the goal, the
physical presence of a moving body with a human shape makes all the
difference” (Wicke and Veale, 2018), hence stressing the effect of
_organismoid (humanoid) embodiment_. Moreover, they hypothesise that a
“listener that can identify with the storyteller is better positioned to
empathize with the story that the teller wants to convey, especially when that
story is crafted from the life experiences of the listeners themselves”. This
reliance on life experience resonates with Colton, Pease and Saunder’s (2018)
previously mentioned factors to improve the authenticity of CC systems.

#### Intentionality & Autonomy

While _organismic embodiment_ is rarely addressed in the literature and
typically only through human embodiment (Kirsh, 2011; Alexandre, 2020; Loesel,
Mirowski, and Mathewson, 2020), Guckelsberger et al. (2016) highlight that
_organismic embodiment_ realised in artificial systems might play a central
role in future CC research. Their theoretical investigation sets out by
considering non-artistic creativity in simple computational systems through
the lens of autopoietic enactivism (Maturana and Varela, 1987) as adopted in
the theory of enactive AI (Froese and Ziemke, 2009). The latter theory holds
that machines with organismic embodiment can, similar to living beings,
realise an intrinsic purpose by maintaining the precarious existence induced
by this form of embodiment. It moreover claims that this intrinsic purpose can
ground intentional agency. Guckelsberger et al. (2016) consider more
specifically when organismic embodiment can ground intentional creative
agency, realising genuine creative autonomy (Jennings, 2010). They argue that
a machine grounds value and novelty in creative activity through the
maintenance of their precarious identity, based on acts of self-production and
adaptation against entropic forces. The claim that intentional creative agency
is contingent on _organismic embodiment_ allows for additional, radical
statements. Guckelsberger et al. (2016) argue via Dreyfus (2007) that a CC
system might have to accurately reproduce human _organismic embodiment_ to
exhibit human-like creativity with intentional agency. Moreover, they
introduce the concept of “embodiment distance” to put forward hypotheses on
the impact of _organismic embodiment_ on the perception of creativity: “when
we evaluate the creativity of non-human systems with intentional agency, we
are likely to misjudge value in their behaviour or artefacts, or hesitate to
attribute any value at all, as our embodiment distance is too large”
(Guckelsberger et al., 2016). This makes the mimicking of _organismoid_ , and
potentially, _humanoid embodiment_ relevant. Colton, Pease, and Saunders
(2018) extend the concept of embodiment distance to non-organismically
embodied systems and discuss ways to overcome it to foster the perception of
creativity and authenticity in CC systems.

### Challenges of Embodied CC

Our analysis exposed that many opportunities for embracing embodiment in CC
have a flip-side, the impact of which is mediated by the respective embodiment
type. The identified three challenges are easily overlooked, as they are often
addressed separately from the corresponding opportunities.

#### Computational & Design Costs

While affording a range of opportunities, such as more natural interaction,
the stimulation of creativity, grounding, etc. – being embedded in our
physical world also puts high demands on [the] hardware, software and system
engineering (Saunders et al., 2010) of _physically embodied_ agents.
Fitzgerald, Goel, and Thomaz (2017) particularly lament the increased
processing costs due to the high dimensionality of robot sensors and
actuators. Gemeinboeck and Saunders summarise that “embodying creative agents
and embedding them in our everyday or public environment is often messier and
more ambiguous than purely computational simulation” (Gemeinboeck and
Saunders, 2013). Especially when tempted by the opportunity to outsource
computation into our physical environment, these costs must be carefully
weighted.

#### Unpredictability

Related, creative behaviour that emerges from the interaction of _any
embodied_ agent and their environment, especially if resulting from intrinsic
motivation, is often hard to predict (Guckelsberger et al., 2016). This is
more relevant in some application domains than others, with many artistic
domains affording unique possibilities for playful experimentation. Across
domains however, researchers must exercise particular caution when designing
for interaction with people. Crucially, _virtual embodiment_ comes with more
well-defined interaction interfaces and affords more control in experiments
that can be reset and afford stronger introspection.

#### False Expectations

Several authors express hope that _organismoid_ , in particular _humanoid
embodiment_ can facilitate stronger identification, empathy and affect (e.g.
Wicke and Veale, 2018). Saunders and Gemeinboeck however warn that humanoid
robots can cause disappointment as they “elicit human investment based on
superficial and often false social cues” (Saunders and Gemeinboeck, 2018).
Referencing studies of human-robot interaction (Dautenhahn, 2013), they
particularly highlight the risk of shaping human expectations in a robot’s
social capabilities based on appearance alone. As a workaround, they suggest
focusing research efforts on non-anthropomorphic robots, and on generating
embodied natural movement as means of identification, instead of a similar
form or sensorimotor equipment.

## Discussion of Embodiment Assessment

We faced several challenges in assessing the type of embodiment in the
selected papers. We briefly elaborate on embodiment (i) classification
challenges, (ii) biases, (iii) “under-” and (iv) “over-attributions”, and (v)
typology limitations.

The classification (i) of embodiment was complicated by unspecific
descriptions especially in theoretical papers. Moreover, some papers related
to several systems at once, or exclusively addressed human rather than machine
embodiment. Sometimes the lack of specifics did not allow us to gain insights
into the use of embodiment in CC. Schorlemmer et al. (2014) for instance only
appeal to embodied cognition in a side-note. Affected contributions are listed
in Table 1 without an assessment of their embodiment type.

Biased views of embodiment (ii) were expressed when authors only explicitly
recognised embodiment in humans, e.g. in the case of a virtual system
affecting the embodiment of a human user, or when discussing the challenges of
modelling human movement. An example of this can be seen in Schubert and
Mombaur (2013), who attempt to capture the movement dynamics of human
painters.

Some authors “under-attributed” (iii) embodiment in that they leveraged a
specific embodiment without explicitly referring to its type or properties. We
observed this particularly often for _organismoid embodiment_ , e.g. in
Saunders et al.’s (2010; 2013) description of the _Curious Whispers_ robot
society. Here, the robots’ bug-like, organismoid embodiment is not discussed
explicitly, although it may have a specific effect on the perception by a
human interaction partner.

Closely related, other authors “over-attributed” (iv) embodiment in that they
explicitly appealed to a certain type of embodiment without fully implementing
or discussing its required properties. For example de Melo and Gratch (2010)
describe an experiment performed on “virtual humans”, but they are used as
passive mannequins to shine light on. Given that a simulated environment to
perturb and be perturbed is absent, we cannot even attest _structural
coupling_. Again, this was particularly evident for _organismoid embodiment_.

Limitations of our typology (v) made it challenging to identify specific
embodiment types, in particular _historical embodiment_ : several systems
presented memory or learning capabilities, but this learning did not
necessarily happen during the systems’ lifetime. This launched a discussion
amongst the authors on whether e.g. the existence of learning hardware is
indicative of historical embodiment. We eventually agreed that historical
embodiment is independent of a lifetime criterion or learning, and present if
the system’s past structural coupling has an effect on its future coupling,
e.g. when a past perception triggers a change in a virtual or physical sensor,
e.g. affecting a camera’s angle, thus influencing future perceptions.
_Organismoid embodiment_ was also difficult to assess, as the proximity of a
system in shape and sensorimotor equipment to living beings is a continuum.
Given the focus of many systems on humanoid embodiment, it would have been
helpful to consider it a separate type.

Some types of embodiment turned out to be worse at differentiating and
distinguishing existing work and insights than others, but this does not
necessarily disqualify them from future use. _Structural coupling_ for
instance is the most inclusive type of embodiment and applied to almost all
reviewed systems, in particular to any physical object; yet, it proved
valuable for sanity-checking whether a simulated system can be considered
embodied at all. _Organismic embodiment_ in contrast was the most exclusive
type. We think it should be included nonetheless, given that existing
theoretical work (Guckelsberger, Salge, and Colton, 2017) assigns it a major
role in future CC research, e.g. on creative autonomy. As for any other type,
the current lack of differentiating power might indicate an under-appreciation
in existing work, rather than a weakness of the type itself.

## Directives for Embodied CC Research

Our analysis supports the hypothesis which motivated this systematic review in
the first place: creativity in artificial systems, and how it is perceived, is
affected by embodiment. If we assume this hypothesis, then furthering our
insights into embodied CC will play a critical role in advancing the goals of
CC more generally. However, existing research is highly fragmented and
ambiguous, lacks generalising empirical results, and rarely trades-off the
opportunities and challenges of a certain type of embodiment. We translate
findings from our review on RQ1, RQ3 and RQ2 into three directives as pillars
of a future embodied CC research agenda.

#### Clarify embodiment:

Our review highlighted that many opportunities and challenges of embodied CC
are exclusive to a specific type of embodiment. However, we experienced
serious difficulties in assessing the specific type of embodiment embraced in
existing work, as documented in the corresponding papers. In order to
establish an efficient, unambiguous, verbal and written scientific discourse
on embodied CC, we urge researchers to always clarify what specific embodiment
they appeal to in a particular theoretical or applied project. To this end,
adopting definitions from typologies as presented here may serve as a
shortcut, foster comparison, and alleviate the “under-” or “over-attribution”
of a certain embodiment. However, any typology can be contested and lacks
detail; references to specific embodiment types should thus always be
complemented with extensive descriptions.

To counteract the fragmentation of research output, we recommend to reference
embodiment-related work extensively. This review, albeit non-exhaustive, might
serve as a starting point to identify relevant work.

#### Conduct empirical studies:

Our review moreover uncovered that no existing CC study produced generalising,
empirical insights about the effect of embodiment on (i) an artificial
system’s creativity, and (ii) how its creativity is perceived by others,
including humans. Researchers either make assumptions on this relationship, or
draw on existing empirical findings from other fields that may not easily
translate to computational systems (e.g. Brown, 2016). We recommend to conduct
qualitative and quantitative empirical studies on the impact of a specific
embodiment, treated as independent variable, on (the perception of)
creativity. Informed by our analysis of which embodiments differentiated
existing work most, we recommend that initial empirical studies should
investigate _virtual_ and _physical_ , or _humanoid_ and other variations of
_organismoid embodiment_ , as values of the independent variable.

In evaluating the perception of creativity (ii) as dependent variable,
experimenters must eliminate or weight for creative ability (i). Vice versa,
in evaluating creative ability (i), they must avoid bias by the perception of
creativity (ii), e.g. by employing objective measures of creativity (Ritchie,
2007). When employing subjective measures, researchers should consider
concepts introduced in the literature such as the _embodiment gap_
(Guckelsberger, Salge, and Colton, 2017; Colton, Pease, and Saunders, 2018)
between the system and its evaluator(s) as mediating variable.

#### Trade-off opportunities and challenges:

We encourage researchers to eventually make use of these empirical insights
for the design of systems that reliably leverage a certain embodiment as a
means to an end, e.g. to accurately model creative processes that emerge from
sensorimotor interaction in a specific domain. Crucially though, our review
showed that most embodiment-related opportunities come with a challenging
flip-side. In order to avoid an unfavourable trade-off, we recommend
researchers to always inform their choice of a particular embodiment not only
by its opportunities, but also by the corresponding challenges.

## Conclusions and Future Work

Motivated by the potential impact of embodiment on creativity and its
perception in artificial systems, we set out to map the present landscape of
embodied computational creativity (CC), and offered directions for future
research in this area. To this end, we conducted a systematic review of papers
presented at the International Conference on Computational Creativity (ICCC)
that explicitly discuss embodiment. To counteract ambiguity in the concept’s
use, we adopted a well-established embodiment typology, and extended it based
on the recent scientific debate. We found that most existing work can be
differentiated by its focus on _virtual_ vs.  _physical_ , and, more fine-
granularly, on _humanoid_ vs.  _non-humanoid, organismoid embodiment_.
Moreover, we showed that each type comes with its unique opportunities and
challenges as flip-sides of the same coin. Overall, we identified nine
opportunities, e.g. the outsourcing of computation or support for more natural
interaction, and three challenges, e.g. unpredictability and the shaping of
false expectations, from existing studies. We identified several shortcomings
of existing work that likely hinder progress on embodied CC research, most
prominently ambiguity in the use of the embodiment concept and a lack of
dedicated empirical research. We leverage these insights in our final
contribution: three directives to advance embodied CC research.

While we chose the scope of this study to provide a reasonably unbiased, big-
picture view of embodied CC, future work should be dedicated to incorporating
additional, relevant references. How to meaningfully constrain the scope is an
open question, as relevant work can not only be found in be found in CC books,
but also related fields such as videogame AI, robotics, design and art. To
allow for fairer and more direct, unambiguous comparisons, we moreover suggest
to complement the present review methods with interviews, in which researchers
in embodied CC are asked directly to describe the embodiment in their concrete
system or theory. We also deem it worthwhile to conduct a separate, deeper
investigation of embodiment in co-creative systems and creative system
societies, drawing on theories of embodied, embedded, extended and enactive
social cognition (e.g. Barsalou et al., 2003; Dautenhahn, 1997; De Jaegher and
Di Paolo, 2008), and on embodied interaction (e.g. Dourish, 2001) as well as
embodied aesthetics research (e.g. Scarinzi, 2015). Together with these
extensions, our review and the extended typology could eventually benefit a
longitudinal analysis of embodied CC, identifying trends in the attitudes
towards using specific embodiment types and their associated opportunities and
challenges over time and across venues. We hope that this paper provides the
necessary knowledge, inspiration, and guidance to drive future research on
embodied computational creativity (CC).

## Acknowledgements

We thank our reviewers for their valuable feedback, and Rob Saunders for
commenting on the camera-ready paper. We are grateful to Marcus Scheunemann
for advising us on social embodiment. CG is funded by the Academy of Finland
(AoF) flagship programme “Finnish Center for Artificial Intelligence” (FCAI).
AK & TT are funded by the AoF project “Digital Aura” (#311090). SNY is funded
by CONACyT, project “ReNACE” (CB A1-S-21700).

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  *[CC]: computational creativity
  *[ICCC]: International Conference on Computational Creativity