# Heritability in Morphological Robot Evolution

Matteo De Carlo  Vrije Universiteit Amsterdam
Amsterdam, Netherlands
<EMAIL_ADDRESS>Eliseo Ferrante  Vrije Universiteit Amsterdam
Amsterdam, Netherlands
Technology Innovation Institute
Abu Dhabi, United Arab Emirates
<EMAIL_ADDRESS>Daan Zeeuwe  Vrije Universiteit Amsterdam
Amsterdam, Netherlands
<EMAIL_ADDRESS>Jacintha Ellers  Vrije Universiteit Amsterdam
Amsterdam, Netherlands
<EMAIL_ADDRESS>Gerben Meynen  Vrije Universiteit Amsterdam
Amsterdam, Netherlands
<EMAIL_ADDRESS>A.E.Eiben  Vrije Universiteit Amsterdam
Amsterdam, Netherlands
<EMAIL_ADDRESS>
###### Abstract

In the field of evolutionary robotics, choosing the correct encoding is very
complicated, especially when robots evolve both behaviours and morphologies at
the same time. With the objective of improving our understanding of the
mapping process from encodings to functional robots, we introduce the
biological notion of heritability, which captures the amount of phenotypic
variation caused by genotypic variation. In our analysis we measure the
heritability on the first generation of robots evolved from two different
encodings, a direct encoding and an indirect encoding. In addition we
investigate the interplay between heritability and phenotypic diversity
through the course of an entire evolutionary process. In particular, we
investigate how direct and indirect genotypes can exhibit preferences for
exploration or exploitation throughout the course of evolution. We observe how
an exploration or exploitation tradeoff can be more easily understood by
examining patterns in heritability and phenotypic diversity. In conclusion, we
show how heritability can be a useful tool to better understand the
relationship between genotypes and phenotypes, especially helpful when
designing more complicated systems where complex individuals and environments
can adapt and influence each other.

##  I Introduction

Evolutionary robotics (ER) employs several elements of biological evolution to
obtain creative and novel solutions to practical problems. A key concept in
evolutionary biology that could help in designing evolutionary robotic systems
and that so far has remained largely unexplored in evolutionary robotics is
the notion of heritability [7, 23]. Heritability is one of the factors
determining the ability of traits to evolve. A common use of heritability is
in animal and plant breeding, where the response to selection can be predicted
as the product of heritability and the selection differential [15].

Heritability denotes the proportion of additive genetic variance relative to
the total phenotypic variance, and can vary between zero and unity. Hence it
indicates the degree to which the trait is responsive to selection. Low
heritabilities are often associated with a more diffuse genotype-phenotype
map, possibly resulting from a strong influence of the environment on the
phenotype or interaction among genes in their expression (epistasis). Because
low heritability compromises the evolutionary response, the notion of
heritability can be a useful addition to evolutionary robotics as an a priori
evaluation of the evolutionary potential of a system.

One important design choice that has a major impact but is often overlooked
when designing an ER system is the choice of encoding. There are several
encodings that have been used in the literature available to the designer, and
many more if one includes all variations. Despite so, our understanding on how
the choice on a particular encoding can influence the evolutionary process is
still very superficial. The main goal of this paper is to investigate the
applicability of the notion of heritability in an evolutionary robotics system
to better understand the relationship between different encodings and the
generated phenotypes.

Heritability can be defined for any given phenotypic trait of the robots,
either related to the robot’s morphology, (i.e. size), or the robot’s behavior
(i.e. speed). Then for any pair of parent robots, we can determine the average
value of the given trait and compare it with the value of this trait in the
child robot. It is important to note that while the traits we consider are
phenotypic, the mechanisms that transfer them to the offspring depend on the
genotypes, specifically, the genetic encoding that specifies that trait and
the recombination operator that shuffles and combines the parental genotypes
into a new one that represents the child. In principle, this means that based
on observable phenotypic properties we can get information regarding genotypic
processes “under the hood”.

The main contribution is the adoption of the concept of heritability to
Evolutionary Robotics and the demonstration of its utility. There are three
important aspects we investigate:

  * •

Whether heritability can be used as a predictor of the evolutionary response
of a system, specifically whether it is related to the rate of evolutionary
change.

  * •

Whether heritability changes over the course of evolution and, if so, if this
effect can be related to any other measurable aspect of evolution.

  * •

Whether heritability can be used to evaluate and compare different genetic
representations.

##  II System description

###  II-A The Robots

(a) Core module

(b) Brick module

(c) Joint module

Figure 1: Robots are build using three types of modules. Starting with only
one Core module, the robot grows by connecting Brick and Joint modules to the
Core or other already connected modules.

The robots evolved in our system is a modular robotic framework based on
RoboGen [1]. Each robot is composed of three different types of modules: one
Core module (Figure 1(a)), an arbitrary number of Brick modules (Figure 1(b)),
and an arbitrary number of Joint modules (Figure 1(c)). The Core module is
unique for each robot and represents the robot “head” that, in the original
physical incarnation [11], contains the main logic board and the battery. The
Core module has four connection points where other modules can be attached.
Brick modules represent the “backbone” of the robot. Only through Brick
modules, the robot can take up arbitrary shapes. Actuation can only be
achieved through the Joint modules, thus Joint modules are the only modules
capable of changing the state of the robot in the environment. Joint and Brick
modules can be attached to any other module in two different ways, which
differ from each other by $90\degree$ for the axis perpendicular to the
attachment plane. In [4], we had already introduced this rotational
attachment, but it applied only to Joint modules. Allowing the Joint to be
attached rotated allows the robot to evolve morphologies that have more
variety in terms of degrees of freedom in terms of actuation. In this work, we
also introduce rotational attachment for Brick modules, which potentially
allows robots that extend also vertically against gravity. In general, the
design allows the inclusion of sensors, but for this study, we do not use any.

###  II-B Robot Brains

The controller of the robots is based on Central Pattern Generators (CPG)
after [12, 13]. Every joint in the body has a corresponding CPG node that
consists of three neurons. Two of these neurons (that we call $x$ and $y$
neurons) are coupled by two-directional connections, one from $x$ to $y$, and
one from $y$ to $x$. By definition, the weights of these connections have the
same value but the opposite sign. The remaining neuron in a CPG node provides
the output signal to the servo motor driving the given joint. The
corresponding weight is set at $1.0$ in each joint, thus a CPG node can be
configured by just one parameter regulating the connections between $x$ and
$y$.

The overall controller architecture is a network with one CPG node for each
joint and a connection between two such nodes if the corresponding joints are
neighbors separated by no more than two empty cells (in the Manhattan sense)
in the 3D Euclidean grid enclosing the robot. Connected neighbor CPG nodes can
synchronize the oscillations of their joints and induce global locomotion
patterns. The number of configurable parameters for a robot brain is thus
$j+c$, where $j$ is the number of joints, and $c$ is the number of connections
between joints; in Figure 2 we show an example of how the nodes would be
connected in a robot made of 8 joints configured as in the ”spider“ robot from
[11].

Figure 2: Brain diagram for a ”spider“ robot with eight joints positioned in a
”plus“ shape.

###  II-C Evolution with tree-based representation

For a direct encoding, we are using a tree-based representation, which in
implementation is very similar to [9, 10].

In a tree-based representation, the genotype is a tree data structure in which
each node represents a module of the robot. Modules differentiate into three
types: Core, Brick, and Joint modules, as represented in Figure 1. The Core
module is always the root of the tree and it can only be present once in the
entire genotype. It can have four children. The Brick module is attached on
one side to its parent block and has three remaining slots available for child
nodes. The Joint module has only one remaining slot for a child module,
therefore it does not allow any branching. A Joint has three extra parameters
that directly encode the oscillator parameters: frequency, offset, and
amplitude. In our tree-based representation, the brain development is limited
to only decode parameters for the oscillators of the CPG network; i.e. all
connection between oscillating nodes are not activated.

The robot module tree can be altered by one of the following mutation
operators. The changes primarily revolve around changing the body: adding a
random module, deleting a sub-tree, duplicating a sub-tree, or swapping a sub-
tree. Alternatively, brain mutations are facilitated by mutating the joint
oscillator parameters to achieve different activation patterns.

Parent robot trees can be recombined by inheriting sub-trees from the parents.
Some checks and balances ensure that the recombined trees are valid, and do
not exceed the maximum limit of modules.

###  II-D Evolution with L-system representation

For an indirect encoding, we choose a system from our previous work [4], which
is composed of an Lindenmayer-system (L-system) [14] that describes body and
brain structure and a component based on Hypercube-based Neuro Evolution of
Augmenting Topologies (HyperNEAT) [22, 6] that encodes the weights of the CPG
network. These indirect encodings are capable of creating symmetrical growth
structures and repetitions in our bodies.

L-Systems are parallel rewriting systems acting on a formal grammar. The
grammar is defined as a tuple $G=(V,w,R)$, where $V$ is the Alphabet, $w$ is
the Axiom and $R$ is a set of Replacement Rules. L-Systems start from the
Axiom $w$, which is a sequence of symbols from the Alphabet. To develop an
L-System grammar, the Axiom is expanded into a longer sentence by replacing
symbols using the Replacement Rules in $R$. The replacement operation can be
repeated multiple times on the sentence.

In this work, we adapted a system from [17], where each genotype is a grammar
with always the same Axiom and Alphabet for all robots. The Alphabet is made
of the following symbols:

  * •

Robot modules: the Core, the Brick, a Vertical Joint, and a Horizontal Joint.

  * •

Mounting commands: $add\\_left$, $add\\_front$, and $add\\_right$. Mounting
commands must be followed by a module symbol otherwise they are ignored. When
the final sentence is read, their role is to attach the following module
symbol in the sentence to the module indicated by the cursor position, at a
new position (left, front, or right) depending on the specific command.

  * •

Moving commands: $move\\_back$, $move\\_right$, $move\\_front$, and
$move\\_left$. The moving commands alter the position of the cursor.

The Replacement Rules of our L-System are a set of rules that replace any of
the robot module symbols with a sequence of new symbols from the alphabet. In
other words, robot module symbols are both terminal and non-terminal symbols
in our L-System. Any other symbol is terminal, which means it cannot be
replaced further. The Axiom of our L-System is a sentence made of a single
symbol: the Core block. Once the L-System grammar develops the Axiom into a
sentence, the sentence is used as a sequence of instructions that describe how
to build the robot.

The system we used here differs from the one we inspired upon, by the addition
of an additional constraint on the morphology, i.e. we do not allow a joint to
be attached to another joint. This extra addition showed in previous
experiments [4] that increase the chances for more complex robots to appear
and we decided to use it here to increase the chances for interesting
morphologies in both experimental configurations. We also improved the
Alphabet with the introduction of a new rotated Brick module, which allows for
the morphologies to develop in three dimensions.

The brain structure is defined by the body; each joint creates a corresponding
CPG oscillator node and connections are made using the rules already explained
in subsection II-B. When all connections are defined, each CPG node is
positioned on a substrate space with $x,y,z,w$ coordinates and each connection
weight value is queried from a CPPN [21], as defined by the HyperNEAT
algorithm. In the substrate space, $x$, $y$, and $z$ determine the position of
the CPG’s node corresponding joint, while the $w$ axis determines the front
and back neurons in an oscillating CPG node.

The mutation and crossover operators are defined by their individual
components: for the L-system component, we use the operators defined in [16].
For the HyperNEAT component, we use the operators as defined by HyperNEAT,
with the exclusion of the species, i.e. genomes are not divided into species
and crossover is possible for each pair of genome in the population.

##  III Methodology

###  III-A Heritability

In an evolutionary system the phenotypic variation ($V_{P}$) of a population
of individuals is an expression of genetic variation ($V_{G}$) and
environmental factors ($E$).

$V_{P}=V_{G}+E$ (1)

The genetic variation can be further subdivided into three major components:
additive genetic variation ($V_{A}$), non additive genetic variation caused by
epistatic genes ($V_{NA}$) and effects of random mutations ($M$).

$V_{G}=V_{A}+V_{NA}+M$ (2)

As defined in [23, 7], heritability measures the contribution of genes to
phenotypic traits. Each phenotypic trait has a different value of
heritability. Heritability can be defined as broad-sense heritability
($H^{2}$) or narrow-sense heritability ($h^{2}$). Broad-sense heritability is
the proportion of phenotypic variation that is created by the genetic
variation. Narrow-sense heritability is only the proportion of genetic
variation that is generated by additive genetic values, not including any
effect of dominance or epistasis.

$H^{2}=\frac{V_{G}}{V_{P}}\hskip 28.45274pth^{2}=\frac{V_{A}}{V_{P}}$ (3)

Heritability is also an important component of the “response to selection”
($R$), a value that can be predicted as the product of narrow-sense
heritability and selection differential ($S$) [15]:

$R=h^{2}\cdot S$ (4)

The value of heritability can be calculated from its theoretical formula, but
this requires a deep mathematical understanding of our genotype model.
However, if we are only interested in the additive genetic material, an
estimate of narrow-sense heritability can easily be derived from population
measurements by linearly regressing the average trait value of the offspring
against the parental phenotype. An approximation to a linear model is possible
because the additive genetic code has a linear response to the resulting
phenotype, in contrast to epistatic genetic code which has a much more
unpredictable effect on the phenotype. The value for the slope of the linear
regression is our numerical estimation for heritability. The value for
heritability can vary between $1.0$ and $0.0$, where $h^{2}=1.0$ is a
$45\degree$ linear regression, representing a perfect match between parents’
average trait and the offspring’s trait. A value of $h^{2}=0.0$ instead
represents a scenario where the offspring’s trait is completely unpredictable
given the parents’ traits.

In biological systems, heritability can isolate those phenotypic features that
are expression of genetic material from features that are influenced by the
environment. Estimated heritability for life history traits and behaviour are
typically low to medium (ranging up to $0.30$ [5]), whereas morphological
traits are often found to have higher heritability (average $h^{2}=0.46$
[20]).

In evolutionary robotics, the influence of the environment over the
development of the individuals is usually very limited, excluding a few
exceptions [2, 18]. In our case the environment is a flat terrain, therefore
its influence is completely absent. The implication is that the phenotypic
variation in this system is only an expression of genotypic variation. Through
linear regression we can estimate the narrow-sense heritability, which is only
an expression of additive genetic variation. The rest of the phenotypic
variation can only be an expression of epistatic gene interaction and mutation
(Eq. 2).

###  III-B Robot Traits

To estimate an overall heritability of our system, we chose a wide variety of
phenotypic traits that are representative of different aspects of our robot.
This work is interested in the overall evolution of modular robots, but the
approach is not limited to any particular number or type of traits. The same
study can be repeated on any trait, e.g. it would be interesting to study the
heritability of “the number of feet in a robot” and what parameters increase
the transmission of the trait to the offspring.

We recorded a set of many traits derived from the descriptors found in [18].
From the many traits available, we sampled only a significant few that we
found to be orthogonal to each other in previous work [3]: some traits that
measure the morphological aspect of the robots and some that measure the
behavioural aspect.

The Morphological traits give us insight on how the robot shapes evolve. In
this work we used:

  * •

_Proportion_ : considering the 2D bounding box that encompasses the robot when
viewed from above, this trait is the ratio between the two sides of this
rectangle.

  * •

_Size_ : the number of modules in the body.

  * •

_Number of Limbs_ : considering the robot as a tree of modules, it is the
number of leaf modules. The value is normalized per robot by the number of all
possible limbs available.

  * •

_Coverage_ : considering the 3D bounding box that encompasses the robot, this
trait is the ratio between the area that is occupied by modules and the total
area of the rectangle.

The Behavioural traits are very important because they give insight in the
complex relationship of body and brain. In this work we used:

  * •

_Speed_ : Describes the average robot speed ($cm/s$), and is calculated as if
the robot took the shortest path from the start position $s_{0}$ to the end
position $s_{t}$, and is defined with Eq. 5.

$v_{disp}=\frac{s_{T}-s_{t_{0}}}{\Delta{t}}$ (5)

  * •

_Balance_ : We use the rotation of the head in the $x$–$y$ plane to define the
balance of the robot. We describe the rotation of the robot with three
dimensions: roll $\phi$, pitch $\theta$, and yaw $\psi$. Thus, we consider the
pitch and roll of the robot head, expressed between $0\degree$ and
$180\degree$ (because we are not interested in whether the rotation is
clockwise or anti-clockwise). Perfect balance corresponds to
$\theta=\phi=0\degree$, so that the higher balance, the less rotated the head
is. Formally, balance is defined by Eq. 6.

$b=1-\frac{\sum^{T}_{t=1}{|\phi_{t}|+|\theta_{t}|}}{180\cdot 2\cdot\Delta{t}}$
(6)

##  IV Setup

For both encodings we use the same evolutionary algorithm with a generational
population update scheme, that is, an evolutionary algorithm where consecutive
populations are non-overlapping. This means that survivor selection is
trivial: no members of population $P_{n}$ survive, the subsequent generation
$P_{n+1}$ consists of offspring of the current one. As for parent selection,
we use the tournament selection mechanism with a tournament size of two
individuals. This represents a low selection pressure. We run this algorithm
with a population size of 100 individuals for 50 generations, amounting to a
total of 5000 evaluations as the computational budget for optimizing the
robots’ makeup. For both encodings, fitness evaluations are done by placing
the given robot on a flat surface and running it for 30 seconds. We evolved
the robots for movement, using the _speed_ behavioral trait as the value for
fitness. We adjusted the mutation rates for the evolutionary run to be quite
high, with a probability of $0.59$ of having at least a body mutation for the
tree-based representation and we used the same probability ($0.59$) for the
mutation chance on the L-System grammar.

##  V Results Analysis

| Heritability
---|---
Trait | Tree-based | L-System
Speed | 0.74 | 0.35
Balance | 0.77 | 0.37
Proportion | 0.65 | 0.41
Size | 0.73 | 0.47
N. of limbs | 0.83 | 0.67
TABLE I: Estimated values of heritability for each phenotypic trait divided

(a) Heritability of tree-based encoding for the speed trait. $H^{2}=0.74$.
Blue line is the resulting linear regression (slope is heritability). Red line
is a perfect heritability reference. We can observe that even if points are
more concentrated, they have an higher heritabiltity compared to the tree-
based encoding.

(b) Heritability of L-System encoding for the speed trait. $H^{2}=0.35$. Blue
line is the resulting linear regression (slope is heritability). Red line is a
perfect heritability reference. We can observe that even if points are more
scattered, they have a lower heritabiltity compared to the tree-based
encoding.

(c) Median speed per generation

(d) Heritability per generation

(e) Increment of the median speed per generation

(f) Median diversity of speed per generation

Figure 3: Analysis of behaviour. Here we consider the evolution of the speed
trait, how fast it changes per generation, speed heritability at the first
generation and how it changes during evolution, and the diversity of speed
present across the population.

(a) Heritability of tree-based encoding for the “number of limbs” trait.
$H^{2}=0.83$. Blue line is the resulting linear regression (slope is
heritability). Red line is a perfect heritability reference. We can observe
that points are well clustered around the $45\degree$ line, resulting in a
pretty high value of heritability.

(b) Heritability of L-System encoding for the “number of limbs” trait.
$H^{2}=0.67$. Blue line is the resulting linear regression (slope is
heritability). Red line is a perfect heritability reference. We can observe
that points are scattered all around the plot, resulting in a lower value of
heritability.

(c) Median number of limbs per generation

(d) Heritability per generation

(e) Increment of the median number of limbs per generation

(f) Median diversity of number of limbs per generation

Figure 4: Analysis of morphologies. Here we consider the evolution of the
“number of limbs” trait, how fast it changes per generation, the trait’s
heritability at the first generation and how it changes during evolution, and
the diversity of the “number of limbs” present across the population. The
value for the “number of limbs” is normalized to the maximum possible number
(of limbs) for each robot.

(a) Balance: Tree-based

(b) Proportion: Tree-based

(c) Size: Tree-based

(d) Balance: L-system

(e) Proportion: L-system

(f) Size: L-system

Figure 5: Scatterplot for the two encodings for the parents in the first
generation and their offspring. The x axis shows the average trait value of
the parents, the y axis shows the value of the offspring. The blue line
represents the linear regression of these values. A steeper line indicates a
higher level of heritability, to a theoretical maximum of 1 ($45\degree$
slope). The red line is a $45\degree$ reference line.

Our first analysis aims at measuring the heritability of various traits in the
two encoding schemes at the start of the evolution experiment. To do so, for
each encoding/trait pair, we measure heritability using data from all
evolutionary runs, but only on the very first generation. Heritability is
measured comparing the trait value of an offspring against the average of the
parents, therefore we need the offspring from the second generation as well. A
linear regression is applied to the trait values of parents against offspring,
and the slope of the resulting linear model is our estimate of heritability.
The estimated values from our measurements are reported in Table I. The
scatter plots used for estimating heritability can be seen in Figures 3(a),
3(b), 4(a), 4(b) and Figure 5. As we can see, the Tree-based encoding
consistently shows higher values of heritability for all traits.

###  V-A Relationship between heritability and initial evolutionary response

We aim at analyzing the relationship between heritability and evolutionary
response for the two encoding schemes. To achieve this, we analyze one
behavioral and one morphological trait using a common scheme, as used in
Figure 3 and Figure 4. Namely, in Panels (a) and (b) of the figures we will
compare the heritability for direct vs indirect encoding calculated only in
the first generation. In Panels (c) and (e) we show the dynamics of the trait
being considered. Panel (c) shows the value of the trait over generations
while Panel (e) shows its rate of change (or derivative) over generations. To
further understand how heritability can explain the trait dynamics over
generations, we use Panels (d) to highlight the evolution over generations of
the heritability metric (calculated across two consecutive generations) and
Panel (f) to highlight the evolution over a generation of the phenotypic
diversity of the trait being considered within the population.

Figure 6: Distribution of fitness in the random initial population

We first analyze the most important trait, since this is the one that is under
selection: speed. In Figure 3(c) and Figure 3(e), we observe that in the first
$10$ generations of the evolutionary process the Tree-based representation has
a higher rate of change in fitness compared to the L-System. This finding is
further confirmed by looking at the fitness distribution at Generation $0$ for
all runs and for the two encodings, shown in Figure 6. Here we see that the
L-System even starts with an advantage, represented by the much higher fitness
diversity in the initial population and the corresponding presence of higher
fitness individuals. Despite this, the L-System experiment evolves initially
at a slower rate than the Tree-based experiment (as in Figure 3(e), which
means that those high-fitness individuals present in the populations are not
able to pass their phenotype to their offspring to the same extent as it
happens in the Tree-based representation. We argue that the concept of
heritability can shed light to understand better what we observed above.
Indeed, the Tree-based encoding has a higher heritability value than the
L-System encoding (see Figure 3(a) and Figure 3(b)). Heritability can inform
us on how much of the phenotypic trait variation will be passed on from
parents to offspring, therefore high heritability at the beginning of the
evolutionary process can predict a higher rate of change in the trait under
selection, as is happening in our system. Thus, higher heritability at the
beginning of the evolutionary process directly facilitates the effect of
initial selection, because good parents have a higher probability of creating
good offspring.

Importantly, the relation between initial heritability and the initial rate of
change of a trait is not true only for traits that are specifically under
selection. To support this claim, we perform the same analysis for all traits,
and we report here only one example. Figure 4(e) shows the rate of change of a
morphological trait that is not under selection (number of limbs). Also here,
we observe the same overall pattern: the Tree-based representation has a
higher initial rate of change in this trait, consistently with having higher
heritability (Table I).

###  V-B Heritability during the later phase of evolution

The analysis of heritability can not only help to describe the behavior of
evolutionary systems in their initial phases, but it can also show interesting
patterns during later advanced phases of their process. From the theoretical
definition, we expect the estimated value of heritability to be constant over
the course of evolution, under the condition that the selection process does
not affect the genetic variation in the population. Surprisingly, by computing
the estimated value of heritability for each of the generations, shown in the
top row of Figure 9, we observe changes of the estimated heritability in our
experiments. The change in heritability value across generations is most
evident for traits that are not under selection. When looking at Figure 9, we
observe in the first generations a tendency for heritability to decrease for
the Tree-based representation and to increase for the L-System representation.
In the later stages of evolution heritability stabilizes for the tree-based
representation and becomes highly unstable for the L-system genotype.

Figure 7: Diversity per generation. Diversity is the average distance of one
individual against all other individuals in the same population. Distance is
calculated in the trait space.

The change of heritability over generations can be explained if we also
analyze how the phenotypic diversity of the population over the generations
varies. The diversity in the L-system population converges to zero quite
quickly in all traits (bottom row of Figure 9), except the one we select for:
speed (Figure 3(f)). In Figure 7 we can confirm and overall loss of phenotypic
diversity for L-system experiments. An overall decrease in phenotypic
diversity in artificial evolutionary systems is often observed when evolution
is converging to a solution and it is caused by a corresponding loss of
genotypic diversity. A change in genotypic diversity can also explain the
change in heritability we measured. A similar pattern can be observed in the
tree-based representation, but the changes in heritability and diversity are
visible on a smaller scale, for fewer generations and smaller changes in
values.

Interestingly, we observed an unexpected overall increase in heritability for
the L-system, as the value for heritability started relatively low and
increased over generations. We hypothesize that selection is responsible for
this effect. The mating selection is probably slowly excluding all individuals
that present highly unpredictable gene sequences. These highly unpredictable
gene sequences can cause very poor offspring to be generated from very fit
parents and vice-versa. Slowly the selection process would pick up these
highly unpredictable gene sequences in their low-fitness state and select them
out of the next generation. This effect throughout many generations would
explain a decrease in diversity and an increase in narrow-sense heritability,
as only the predictable gene sequences consistently survive across multiple
generations and predictable gene sequences have cause high heritability in the
population by definition.

Figure 8: Scatter plot and heritability for balance of the last generation for
the L-system configuration. The x axis is the average balance of the parents
and the y axis is the value of balance for the offspring. The blue line
represents the linear regression of these values. A steeper line indicates a
higher level of heritability, to a theoretical maximum of 1 ($45\degree$
slope). The red line is a $45\degree$ reference line. The linear regression
results to almost a $45\degree$ line because there are many overlapping points
in the plot that indistinguishable but influence the regression results.

Another pattern that we can observe is that heritability becomes highly
unstable in later stages of evolution for L-system experiments; this is
especially obvious in Figure 9(a). The explanation for this effect can be
found by looking at the diversity (Figure 9(d)). A decrease in diversity for a
trait is an indicator of a decrease in overall genotypic diversity, which
implies that the evolutionary process is not exploring the search space any
more and all solutions are very similar. But to compute an accurate estimation
of heritability we need high diversity of the population, otherwise we are
computing the linear regression of a concentrated cloud of points, as shown in
Figure 8. By contrast, we deduce that the tree-based experiments are exploring
the evolutionary space and having difficulties in exploiting the solution
space. With this knowledge we can estimate that the tree-based representation
needs a lower mutation rate and a stronger selection pressure to be able to
exploit the solution space. This was to be expected because we choose very
high mutation rates and a relaxed selection mechanism. On the contrary
L-system seems to drop diversity as early as generation 20, which is pretty
early. This is especially noticeable for trait which we don’t select for
(Figures 9(d), 9(e), 9(f)). Our L-system encoding seem to have difficulties to
explore even from the early phases of evolution, despite using evolutionary
parameters that encourage exploration. This suggest that an evolutionary
process using our L-system encoding has a hard time escaping a few local
optima found at the beginning, and probably needs some additional elements to
encourage exploration.

(a) Balance Heritability

(b) Proportion Heritability

(c) Size Heritability

(d) Median diversity of balance per generation

(e) Median diversity of proportion per generation

(f) Median diversity of size per generation

Figure 9: plot of Heritability and Diversity over generations for three
different phenotypic traits: Balance, Proportion and Size

##  VI Concluding remarks

In this paper we introduced the biological notion of heritability as a novel
tool to study encodings in evolutionary robotics. Heritability captures the
correlation between a quantifiable phenotypical trait measured in the parents
and the one measured in the offspring. In our experiments we show that
heritability can be a useful tool in evolutionary robotics to support the
genotype design process. We used this novel tool to tackle the bootstrapping
problem, because it reveals how exploratory a system is during the initial
phases of evolution. We observed how towards the course of evolution, changes
in heritability could correlate to changes in diversity; i.e. in our tree-
based system diversity and heritability seem to stabilize, while, in the
L-system experiments, diversity drops and the estimated heritability increases
at first following by high instability. We related the different rates of
heritability and diversity to different behaviours of exploration and
exploitation and how these concepts seem to be intertwined, i.e. we observed a
correlation between exploratory behaviours and high narrow-sense heritability,
and inverse correlation between exploitative behaviours and low heritability,
caused by a greater epistasis effect in the encoding. Importantly, this
analysis can be performed only within the first few generations (in our case,
$50$), during the transitory phase of the evolutionary process.

Heritability proved to be a helpful tool to evaluate the shape and smoothness
of the search-space, considering the landscape of both fitness and other
phenotypic traits. Tree-based experiments converge to solutions where robots
still retain significant morphological diversity, meaning the local optima
found by evolution in the search-space is a smooth wide hill. This was
expected by a genetic encoding that is mostly made of additive genes. On the
contrary, L-system experiments converged to a single morphological solution
with little-to-none diversity, and by observing the change in heritability and
diversity we can determine that solutions are unlikely to explore other peaks,
probably because they are either too distant, too narrow or not good enough
compare to the solution found. This is an indication that the L-system is a
genetic encoding that contains lots of epistatic effects, meaning many genes
need to align to be able to create a positive effect on the phenotype.

In possess of this knowledge, we will transition to a tree-based direct
encoding in future work where the focus will be on evolution of morphological
traits interacting with other elements, because differences in phenotypical
traits will be more tangible. In other setups, were the interest is in
studying complicated gene interaction, including epistatic effects, L-system
is a good candidate.

This paper also highlights how the notion of heritability draws attention to
major discrepancies between biological and artificial evolutionary systems.
Crucially, in biology computing a value of heritability requires measuring
qualities of the traits at the phenotype level. However, phenotypic traits in
biology are a result of several processes and factors: recombination,
mutation, embryonic development, early life development, behavioural and
morphological lifetime adaptation. Many of these processes can be influenced
by complex environments. In addition, biological systems have and retain high
genotypic variation.

In contrast, artificial systems are extremely simplified. In particular, in
our evolutionary system, individuals develop before they can have any
interaction with the environment. Additionally, when deployed in the
environment, individuals have no morphological or behavioural adaptation
systems at their disposal, no _development_. In addition, our environment
offers minimal interactions with the environment. Artificial systems are also
characterized by a generally lower genotypic diversity and a tendency to
decrease even more over the course of artificial evolution, when the
population converges to a useful solution.

For the future of evolutionary robotics, adding the above-mentioned biological
elements would be very interesting, and some efforts have already been done in
this direction. In [19, 8] we find efforts to introduce learning systems that
enable individuals to adapt to their environment during their lifetime. Some
work can also be found studying how to design a system where the genotype-
phenotype mapping can be influenced by the environment [18]. However, these
additions to the evolutionary process are non-trivial: research on these more
complicated and realistic systems require a substantial increase in
computational cost required from the evolutionary process. This results in
slower iterations and difficulties in the design and parameter tuning
processes, especially if one wanted to study these processes in combination.

The development of artificial life through artificial evolution is still in
his infancy, and lot of work in the above directions and beyond could be done.
Still, both for the current system complexity as well as for the one of future
systems, in our view measuring heritability will be a useful tool that greatly
increases our understanding of the relationship between phenotypes and
genotypes.

## References

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# Addressing the Blind Spots in Spoken Language Processing

Amit Moryossef
Bar-Ilan University
University of Zürich
<EMAIL_ADDRESS>
###### Abstract

This paper explores the critical but often overlooked role of non-verbal cues,
including co-speech gestures and facial expressions, in human communication
and their implications for Natural Language Processing (NLP). We argue that
understanding human communication requires a more holistic approach that goes
beyond textual or spoken words to include non-verbal elements. Borrowing from
advances in sign language processing, we propose the development of universal
automatic gesture segmentation and transcription models to transcribe these
non-verbal cues into textual form. Such a methodology aims to bridge the blind
spots in spoken language understanding, enhancing the scope and applicability
of NLP models. Through motivating examples, we demonstrate the limitations of
relying solely on text-based models. We propose a computationally efficient
and flexible approach for incorporating non-verbal cues, which can seamlessly
integrate with existing NLP pipelines. We conclude by calling upon the
research community to contribute to the development of universal transcription
methods and to validate their effectiveness in capturing the complexities of
real-world, multi-modal interactions.

##  1 Introduction

Human speech is typically accompanied by a dynamic combination of co-speech
gestures and facial expressions, together forming an integral part of human
communication. These non-verbal cues, far from being random or merely
accessory, provide additional layers of meaning, clarify intention, emphasize
points, regulate conversation flow, and facilitate emotional connection. They
enrich our interactions and help convey complex or nuanced information that
words alone might not capture.

Co-speech gestures refer to the hand and body movements accompanying spoken
discourse; they supplement verbal communication by offering additional
information, such as object size or shape; they emphasize and make abstract
concepts tangible, like gesturing upwards to signify an increase; they control
the conversation flow, signaling a speaker’s intent, inviting listener
interaction, or showing that the speaker is in thought or pause; and lastly,
they compensate for the limitations of spoken language, especially in high-
stakes or noisy environments, by providing an alternative mode of conveying
complex or nuanced information.

Facial expressions during speech significantly contribute to communication by
indicating the speaker’s emotions, and providing insight into their feelings
about the topic; they can emphasize certain aspects of the discourse, with
actions like raised eyebrows signifying surprise or importance; they offer
social cues, with expressions like a smile suggesting friendliness or a
serious look indicating sincerity; they help clarify verbal meaning,
especially in ambiguous situations, for example, a confused expression might
denote misunderstanding; finally, they enhance interpersonal connection by
helping to build rapport, expressing empathy, and conveying cues of
understanding and engagement; altogether, facial expressions, like gestures,
add complexity and depth to verbal communications.

The field of Natural Language Processing (NLP) has become highly effective in
understanding language directly from text. However, understanding speech, with
its imperfect and noisy signals, remains a more complex challenge. Text-based
language models have proven highly scalable, thanks largely to the
compressible nature of text and its abundant availability in semi-anonymous
forms. Yet, these models fundamentally ignore the rich layers of meaning added
by non-verbal cues, a significant aspect of human communication. This means
that while we have become adept at parsing text, we are missing out on the
nuanced interplay of speech and gesture that characterizes in-person
communication. Despite some promising work in generating co-speech gestures
from audio Ginosar et al. (2019); Bhattacharya et al. (2021); Liu et al.
(2022), these gestures are often treated as accessory to speech rather than
integral components, and thus, they do not always contribute the correct or
intended information. As such, an understanding and integration of non-verbal
cues remain an important frontier for further exploration in NLP.

Spoken language understanding, we propose, can benefit immensely from the
advances in sign language processing. We advocate for the implementation of
universal automatic gesture segmentation and transcription models that can
transcribe co-speech gestures into textual input. This could be a pioneering
step towards integrating the richness of non-verbal cues directly into the NLP
models. By including transcribed gestures, the models would bridge the blind
spots in spoken language understanding. This is a bidirectional process; Just
as spoken language models can learn from sign language processing, the
insights from the transcription of spoken language gestures can also inform
and enhance sign language processing, due to iconicity, and metaphors.
Ultimately, this holistic approach would result in a more nuanced and
comprehensive understanding of human communication, bringing us closer to the
complexities and richness of real-world, multi-modal interactions.

##  2 Stereotypical Language Variation

Non-verbal forms of communication are subject to significant cultural
variability, shaped by a complex interplay of historical, societal, and
cultural factors.

In Mediterranean cultures, non-verbal communication is prevalent and vibrant.
People in this region often use expressive gestures and maintain close
personal space when communicating. Italian, for instance, is renowned for its
extensive use of gestures. Italians often use their hands and bodies
expressively to illustrate their points or emotions, and there is a broad
range of specific gestures that carry particular meanings, often
comprehensible even without accompanying speech.

In contrast, Japanese communication tends to incorporate fewer and more subtle
non-verbal cues. A bow, a nod, or a slight tilt of the head can convey a
myriad of meanings depending on the context, demonstrating respect, agreement,
or understanding. Meanwhile, in Nordic cultures, such as Swedish or Finnish,
non-verbal cues are typically used sparingly. The communication style tends to
be direct and understated, with less emphasis on gestures and more focus on
verbal content.

Overall, these stereotypical examples highlight the diverse ways in which
languages around the world incorporate non-verbal cues into communication.
This diversity emphasizes the importance of cultural understanding and
sensitivity in interpreting and engaging in cross-cultural communication
research, and data collection and annotation.

##  3 Motivating Examples

Non-verbal cues can act to affirm and reinforce the spoken words, thereby
strengthening the communicated message. They can also undermine the verbal
message, creating a contradiction between what is being said and the speaker’s
true intent or feelings. For NLP research to understand speech, it can not
rely solely on audio (or textual transcription) to understand the intent of
the speaker.

For example, saying ‘Perfect’ while making a circle with the thumb and index
finger often emphasizes approval and satisfaction. Similarly, nodding while
saying ‘Yes’ reinforces affirmation, underscoring the speaker’s understanding
or agreement. On the other hand, saying ‘OK’ while rolling one’s eyes, can
suggest that the speaker doesn’t find the situation truly satisfactory,
despite the verbal agreement. Similarly, stating “I’m not mad” while frowning
or clenching fists suggests that the speaker is indeed upset, contradicting
their verbal assertion.

###  3.1 Machine Translation

While existing in many other languages, Italian stereotypically gives us many
examples of gestures conveying meaning, where the verbal part is often dropped
altogether, making it even more similar to signed languages.

Table 1 showcases a toy example of a conversation between two Italians using
only gestures, without speech. It is transcribed using SignWriting (Sutton,
1990) to demonstrate that anonymous non-verbal transcription can be done in a
low-bandwidth manner and that it can be reproduced and understood by people
trained at reading SignWriting.

|  |  |
---|---|---|---
wth with these two?? |  I know! What the hell? |  Did you hear they’re together?? |  Oh yeah. They’re definitely together!
|  |  |
I shouldn’t be saying this but… she’s cheating on him |  She’s cheating on him?? Woahhh |  Yeah! But it’s none of my business |  I can’t believe it
Table 1: “How to gossip in Italian” by the Pasinis, transcribed in Sutton
SignWriting by Sutthikhun Phaengphongsai
https://www.youtube.com/watch?v=7V-GniCQFkE, demonstrating a conversation
between two Italians using only gestures, without speech.

###  3.2 Sentiment Analysis

To demonstrate the limitations of text-based sentiment analysis, consider the
following hypothetical dialogue between a couple, where the man is utilizing
passive-aggressive communication. In each turn, we also present the sentiment
score as predicted by the Google Cloud Natural Language API
Demo111https://cloud.google.com/natural-language, where scores range between
$[-1,1]$.

Woman: How is it going? (0) Man: I am fine. (0.74) [crosses his arms] Woman:
Did you enjoy dinner? (0.55) Man: It was fine. (0.92) [avoids eye contact,
lips pressed tightly] Woman: Is something wrong? You seem distant. (-0.78)
Man: No, nothing’s wrong. (0.53) [shakes his head slightly, exhales loudly]
Woman: Are you sure? (0) Man: I said I’m fine. (0) [rolls eyes, turns away]

While all the man’s responses register as neutral to positive, his body
language—avoiding eye contact, pressing his lips tightly together, shaking his
head, exhaling loudly, rolling his eyes, and turning away—signals that he may
actually be upset, frustrated, or disengaged. By neglecting body language and
other contextual clues, current models miss out on a significant layer of
human communication, particularly in emotionally charged or complex dialogues.
Such a holistic approach could provide a more nuanced and accurate
understanding of the emotional context and underlying issues, thus enriching
machine-human interactions.

##  4 Methodology

Machine learning techniques that focus solely on text have gained predominance
due to several key factors: the abundance of readily available text data, the
potential for semi-anonymous data collection and processing, the high
bandwidth-to-overhead ratio as a word consumes only a few bytes compared to
kilobytes or more for a second of speech or video, and the ease with which
text can be viewed, edited, and corrected.

Previous efforts have attempted to include other modalities like images
(Razavi et al., 2019), videos (Yan et al., 2021), or audio through the use of
techniques like VQ-VAEs (van den Oord et al., 2017). However, these approaches
often significantly increase the context size, are not transferable across
different systems, and generally require the original signal (like a video) to
be sent for processing. In contrast, our proposal offers a more flexible,
universal, and efficient way to incorporate non-verbal cues directly as text.

###  4.1 Proposal

We propose adopting a universal transcription system for body language, much
like the written system used for spoken languages. This system would
transcribe gestures, facial expressions, and other non-verbal cues into
textual form. The advantages of this approach are numerous:

#### Flexibility in Transcription

Different programs can decide on their own transcription methods, taking into
account local variations and context.

#### Computational Efficiency

Text-based methods require significantly lower computational resources
compared to image or video processing. (Notoriously, GPT-4 was released
without image upload support, since inference on a single image takes upwards
of 20 seconds)

#### Compatibility with Existing Models

As the body language would be transcribed into discrete tokens, it can fit
seamlessly into existing large language models without any modification.

#### Anonymity

Transcription acts as a form of biometric anonymization, removing the need to
share actual video or images.

#### Explainability

The textual transcription provides a more transparent input, making the
language modeling process more understandable.

#### Seamless Integration

The proposed methodology does not require any significant changes to existing
NLP pipelines. It simply acts as an additional layer of data for better
understanding and disambiguation. You can include it, or not.

###  4.2 Implementation

To successfully integrate non-verbal cues when processing spoken language, we
advocate the following steps:

  1. 1.

Capture both video and audio during speech.

  2. 2.

Use sign language segmentation models to identify boundaries of individual
gestures.

  3. 3.

Transcribe these gestures into a textual notation system like SignWriting.

  4. 4.

Use speech-to-text models to transcribe the spoken language, identifying the
boundaries where each word is expressed.

  5. 5.

If word boundaries are not directly accessible, a re-alignment model can be
used to approximate these boundaries.

  6. 6.

Combine both speech and gesture transcriptions into a single text string,
where gestures can be used to provide additional context to the spoken words.

This approach can be thought of as analogous to incorporating additional
context, such as gender, into machine translation (Moryossef et al., 2019). By
training on a large dataset that includes unmarked sentences, the model may
develop certain biases. Introducing a smaller dataset with contextual
information can help the model learn correlations between language and
specific contexts. During inference, one has the option to either provide just
the text for a more generalized output or include additional contextual tags
for a more accurate and targeted output.

##  5 Conclusions

This paper underscores the fundamental role of non-verbal cues, such as co-
speech gestures and facial expressions, in human communication. While strides
have been made in the realm of Natural Language Processing for understanding
textual content, a holistic approach that integrates the rich layers of non-
verbal information is significantly lacking. This shortfall not only hampers
the comprehension of spoken language but also limits our ability to construct
nuanced, context-aware NLP models.

The key to advancing in this frontier may lie in borrowing techniques and
insights from sign language processing. We advocate for the adaptation and
implementation of universal automatic gesture segmentation and transcription
models that can transcribe co-speech gestures into textual input. Such an
approach would be a pivotal step in bridging the gap between text-based and
real-world, spoken interactions, thereby enriching both the scope and
applicability of NLP models.

When processing spoken language content, researchers should adopt a more
holistic lens, one that goes beyond words and phrases to also consider non-
verbal cues. Existing universal segmentation and transcription models used in
sign language can serve as invaluable resources for this purpose, as they
offer the ability to transcribe gestures directly as text.

We call upon researchers in spoken language processing to contribute to the
development of universal gesture transcription methods. Furthermore, we
encourage the academic community to construct challenge sets specifically
tailored to validate the utility of these transcription methods in capturing
the complexities of non-verbal communication. These steps are not merely
supplementary but are central to achieving a more comprehensive understanding
of human communication in its full richness and complexity.

## References

  * Bhattacharya et al. (2021) Uttaran Bhattacharya, Elizabeth Childs, Nicholas Rewkowski, and Dinesh Manocha. 2021\.  Speech2AffectiveGestures: Synthesizing Co-Speech Gestures with Generative Adversarial Affective Expression Learning.  _Proceedings of the 29th ACM International Conference on Multimedia_.
  * Ginosar et al. (2019) S. Ginosar, A. Bar, G. Kohavi, C. Chan, A. Owens, and J. Malik. 2019.  Learning Individual Styles of Conversational Gesture.  In _Computer Vision and Pattern Recognition (CVPR)_. IEEE.
  * Liu et al. (2022) Xian Liu, Qianyi Wu, Hang Zhou, Yuanqi Du, Wayne Wu, Dahua Lin, and Ziwei Liu. 2022\.  Audio-Driven Co-Speech Gesture Video Generation.  _Advances in Neural Information Processing Systems_.
  * Moryossef et al. (2019) Amit Moryossef, Roee Aharoni, and Yoav Goldberg. 2019.  Filling gender & number gaps in neural machine translation with black-box context injection.  In _Proceedings of the First Workshop on Gender Bias in Natural Language Processing_ , pages 49–54, Florence, Italy. Association for Computational Linguistics.
  * Razavi et al. (2019) Ali Razavi, Aäron van den Oord, and Oriol Vinyals. 2019.  Generating diverse high-fidelity images with vq-vae-2.  In _Neural Information Processing Systems_.
  * Sutton (1990) Valerie Sutton. 1990.  _Lessons in sign writing_.  SignWriting.
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  * Yan et al. (2021) Wilson Yan, Yunzhi Zhang, P. Abbeel, and A. Srinivas. 2021.  Videogpt: Video generation using vq-vae and transformers.  _ArXiv_ , abs/2104.10157.