Habilitation Thesis Prague Czech Republic <start date of thesis> <end date of
thesis>

0 black 0.5 10pt 30pt  This habilitation thesis was accepted at FEE, CTU in
Prague in October 2022. This document does not contain the appendices. Full
version with appendices available at
https://dspace.cvut.cz/handle/10467/98714. Cite as: Hoffmann, M. (2021),
’Learning body models: from humans to humanoids’, Habilitation thesis, Faculty
of Electrical Engineering, Czech Technical University in Prague.

Czech Technical University in Prague
Faculty of Electrical Engineering
Department of Cybernetics

Learning Body Models:
From Humans to Humanoids

Habilitation Thesis

Matěj Hoffmann

Prague, November 2021

###### Abstract

Humans and animals excel in combining information from multiple sensory
modalities, controlling their complex bodies, adapting to growth, failures, or
using tools. These capabilities are also highly desirable in robots. They are
displayed by machines to some extent. Yet, the artificial creatures are
lagging behind. The key foundation is an internal representation of the body
that the agent—human, animal, or robot—has developed. The mechanisms of
operation of body models in the brain are largely unknown and even less is
known about how they are constructed from experience after birth. In
collaboration with developmental psychologists, we conducted targeted
experiments to understand how infants acquire first “sensorimotor body
knowledge”. These experiments inform our work in which we construct embodied
computational models on humanoid robots that address the mechanisms behind
learning, adaptation, and operation of multimodal body representations. At the
same time, we assess which of the features of the “body in the brain” should
be transferred to robots to give rise to more adaptive and resilient, self-
calibrating machines. We extend traditional robot kinematic calibration
focusing on self-contained approaches where no external metrology is needed:
self-contact and self-observation. Problem formulation allowing to combine
several ways of closing the kinematic chain simultaneously is presented, along
with a calibration toolbox and experimental validation on several robot
platforms. Finally, next to models of the body itself, we study peripersonal
space—the space immediately surrounding the body. Again, embodied
computational models are developed and subsequently, the possibility of
turning these biologically inspired representations into safe human-robot
collaboration is studied.

###### Acknowledgements.

First, I would like to express thanks to my Humanoid and cognitive robotics
research group at the Department of Cybernetics, Faculty of Electrical
Engineering, Czech Technical University in Prague. The contributions of Karla
Štěpánová, Zdeněk Straka, Petr Švarný, Filipe Gama, Jakub Rozlivek, and Lukáš
Rustler constitute an important part of the thesis. Second, since I joined the
Department of Cybernetics in 2017, the Department has always provided me with
exceptional support. I would like to thank the Dept. Heads (originally Jan
Kybic, later Tomáš Svoboda), Kristina Lukešová for support in HR and other
matters, and Hana Pokorná in procurement. Special thanks go to Tomáš Svoboda
for an absolutely unique combination of support, independence, and mentorship
that he has granted me and my growing research group, and Petra Ivaničová who
has been taking care of the budget of my research group and all administrative
issues related to grant applications and management. Third, I want to thank to
the research groups where I studied and performed research in the past: the
Artificial Intelligence Laboratory, Department of Informatics111The template
used for this thesis is one of Department of Informatics., University of
Zurich (2006-2013) and Italian Institute of Technology (IIT, 2013-2016). The
PhD study at the Zurich AI Lab under Rolf Pfeifer’s supervision has provided a
foundation for my career. Postdoctoral research at IIT and collaboration in
particular with Alessandro Roncone and Ugo Pattacini constitutes another
foundation for my current research. Fourth, I want to thank my external
collaborators, in particular Kevin O’Regan, Jeffrey J. Lockman, Lisa Chinn,
and Daniela Corbetta. Finally, I need to thank my wife Veronika, my daughters
Anna and Tereza, and my parents.

The research reviewed in this thesis would not have been possible without the
generous funding from:

  * •

2020-2024 – Czech Science Foundation (GA CR): “Whole-body awareness for safe
and natural interaction: from brains to collaborative robots”. Project no.
20-24186X.

  * •

2017-2019 – Czech Science Foundation (GA CR): “Robot self-calibration and safe
physical human-robot interaction inspired by body representations in primate
brains”. Project no. 17-15697Y.

  * •

2018-2022 – The Ministry of Education, Youth and Sports of the Czech Republic,
OP VVV funded project CZ.02.1.01/0.0/0.0/16_019/0000765 “Research Center for
Informatics”.

  * •

2014-2016 – Marie Curie Intra-European Fellowship (FP7-PEOPLE-2013-IEF
625727): “iCub Body Schema”.

###### Contents

  1. 1 Introduction
    1. 1.1 Synthetic methodology
      1. 1.1.1 Embodied computational modeling of cognition
      2. 1.1.2 Embodied computational modeling of body representations
      3. 1.1.3 Abstracting general principles of learning body models and applications
    2. 1.2 Body models in humans, animals, and robots
    3. 1.3 Thesis overview
  2. 2 Body knowledge in babies
    1. 2.1 Spontaneous self-touch
    2. 2.2 Localizing touch on the body
    3. 2.3 My contributions
    4. 2.4 Summary
  3. 3 Robots learning body models
  4. 4 Robots learning peripersonal space representations
  5. 5 Robot self-calibration
  6. 6 Safe physical human-robot interaction and collaborative robots

##  Chapter 1 Introduction

The main theme in my research has been embodied cognition and its development,
how it can be studied using robots, and how inspiration from biology can drive
progress in robotic technology. The specific focus is on representations of
the body and the space around it—peripersonal space. This habilitation thesis
is constituted by a collection of thirteen articles (published or accepted for
publication), preceded by a common introductory part that links these works
into a coherent whole.

###  1.1 Synthetic methodology

The methodology employed in my work is the so-called synthetic methodology, or
“understanding by building” [Pfeifer and Scheier, 2001, Chapter 1] [Pfeifer
and Bongard, 2007, Chapter 3]. This approach can be applied to study phenomena
from many disciplines like biology, psychology, or physics. As a matter of
fact, many sciences that were traditionally mostly analytical are becoming
more synthetic, employing computer simulations, for example. Here we focus on
the understanding of intelligence or cognition in particular. The synthetic
methodology, schematically illustrated in Fig. 1.1, unites the following three
goals [Pfeifer and Bongard, 2007, Chapter 3]:

  1. 1.

understanding natural forms of intelligence

  2. 2.

abstracting general principles of intelligent behavior

  3. 3.

building intelligent artifacts

Figure 1.1: Synthetic methodology. Overview of approaches to the study of
intelligence. On the left, we have the empirical sciences like biology,
neurobiology, and psychology that mostly follow an analytic approach. In the
center, we have the synthetic ones, namely cognitive science and AI, which can
either model natural agents (this is called synthetic modeling, the shaded
area) or alternatively can simply explore issues in the study of intelligence
without necessarily being concerned about natural systems. From this latter
activity, industrial applications can be developed. Figure and caption from
[Pfeifer and Scheier, 2001, Chapter 1].

A very influential paradigm in the study of cognition and intelligence was
cognitivism (e.g., [Fodor, 1975, Pylyshyn, 1984]), whereby thinking was
understood as a result of computation over symbols that represent the world.
Such computation in the form of automatic manipulation of abstract symbols was
at the heart of the so-called “Good Old-Fashioned Artificial Intelligence”
(GOFAI) [Haugeland, 1985]. In fact, the computer was not only a tool for
modeling but became a metaphor for the mind. Cognitivism and GOFAI were thus
employing the full synthetic methodology in (1) applying the computer metaphor
to model the mind; (2) abstracting general principles and developing
algorithms for planning, reasoning, etc.; (3) developing applications like
chess-playing programs, expert systems etc.

####  1.1.1 Embodied computational modeling of cognition

While sucessful in the abstract domains, GOFAI faced severe difficulties when
controlling robots that had to interact with the physical world in real time.
Rodney Brooks has openly attacked the GOFAI position in the seminal articles
“Intelligence without representation” [Brooks, 1991b] and “Intelligence
without reason” [Brooks, 1991a]. Through building robots that interact with
the real world, such as insect robots [Brooks, 1989], he realized that “when
we examine very simple level intelligence we find that explicit
representations and models of the world simply get in the way. It turns out to
be better to use the world as its own model.” [Brooks, 1991b] The thesis that
intelligent behavior emerges from the dynamic interplay of brain, body and
environment has also been articulated by the notion of embodiment (e.g.,
[Pfeifer and Scheier, 2001, Pfeifer and Bongard, 2007]) that also started to
challenge cognitivism as the dominant paradigm in cognitive science. Different
variants of embodied cognition theories have been articulated like the dynamic
systems approach to the development of cognition and action [Thelen and Smith,
1994], grounded cognition [Barsalou, 2008], sensorimotor contingency theory
[O’Regan and Noe, 2001], or enaction [Varela et al., 1991].

Embodied cognition theories stressed the constitutive role of the body and
action for even high-level abstract thinking. Given the importance of the body
and closed sensorimotor loops, the analytical and divide-and-conquer approach
of empirical sciences studying specific phemonena in isolation is more
difficult to apply. Instead, a more holistic approach should be adopted.
Applying the synthetic methodology to embodied cognition means not only
modeling cognitive processes but requires building complete artifacts
interacting with the environment. Grey Walter [Walter, 1953] was the pioneer
of this approach already before the era of computers and cognitivism building
electronic machines with a minimal “brain” that displayed phototactic-like
behavior. This was picked up by Valentino Braitenberg who built a whole series
of two-wheeled vehicles of increasing complexity, as summarized in “Vehicles –
Experiments in Synthetic Psychology” [Braitenberg, 1986]. Already the most
primitive ones, in which sensors are directly connected to motors (exciting or
inhibiting them), displayed sophisticated behaviors. The obvious tools for
this type of modeling became robots, giving rise to cognitive developmental
robotics [Asada et al., 2009, Cangelosi and Schlesinger, 2015]. Whenever the
emphasis is on the compatibility with available knowledge on the anatomy and
physiology of the brain structures underlying the behaviors of interest, the
label neurorobotics is also used (e.g., [van der Smagt et al., 2016]).

Let us first focus on the goal of understanding cognition and its development
through syntethic modeling. The perspective of some of the leading researchers
in this field is offered in “The mechanics of embodiment: a dialog on
embodiment and computational modeling” [Pezzulo et al., 2011]. This approach
is also articulated by Caligiore et al. [Caligiore et al., 2010, Caligiore et
al., 2014] and called computational embodied neuroscience, with the following
goals or constraints for the model:

  * •

accurately reproduce the behaviors observed in specific psychological
experiments

  * •

reproduce the learning processes alongside the final behavior

  * •

use architectures and algorithms constrained by neuroscientific evidence

  * •

the model should control an embodied agent

Certain aspects of a natural system are studied, abstracted, and finally
reproduced in an artificial system, which is then subject to investigations.
The behavior as well as developmental and learning processes leading to the
final behavior of the artificial system are compared with the original natural
system, serving as a model of the phenomenon. Not only the overt behavior but
also the inner workings responsible for it are compared. The key question is
what can embodied cognition learn from synthetic modeling. The key benefits,
inspired by Cangelosi and Parisi [Cangelosi and Parisi, 2002], are:

  1. 1.

Explicit and detailed expression of theory. If the theory is “embedded” in a
robot, it has to be explicit, detailed, and complete because otherwise it
could not be tested in the robot.

  2. 2.

Alternative validation. Any theory should ultimately be validated by comparing
it to the biological or psychological phenomenon of interest. However, after
implementing it in a robot, one can verify if it generates the expected
behavior—for example, whether a machine built according to a model of walking
actually walks. Often, this will not be the case and several iterations
improving the theory will follow.

  3. 3.

New and detailed predictions. The behavior of the robot may not match with the
original expectations or may display new unexpected characteristics that can
be compared with existing empirical data or lead to the design of new
experiments.

  4. 4.

Virtual experimental laboratories. With embodied computational models, one
typically has access to all variables, including internal variables (raw and
preprocessed sensory values, internal states, motor commands, etc.). In
addition, it is often relatively easy to manipulate these variables and thus
simulate experiments that would otherwise be impossible or ethically
unacceptable to perform (e.g., simulating sensory or motor impairments).

My account of “Robots as powerful allies for the study of embodied cognition
from the bottom up” is provided in [Hoffmann and Pfeifer, 2018] and included
in Appendix LABEL:appendixA:robots_for_embodied_cognition.

####  1.1.2 Embodied computational modeling of body representations

Synthetic modeling can be also productively applied to the study of body and
peripersonal space representations. Since the focus is on models of the body,
the need for embodied models is even stronger. Humanoid robots possess
morphologies—physical characteristics as well as sensory and motor
apparatus—that are in some respects akin to human bodies and can thus be used
to expand the domain of computational modeling by anchoring it to the physical
environment and a physical body and allowing for instantiation of complete
sensorimotor loops. The iCub humanoid robot [Metta et al., 2010] stands out in
this respect: it has anthropomorphic proportions modeled after a 4-year old
child and a corresponding set of sensory modalities including binocular
vision, hearing, vestibular (inertial) sensing, proprioception, and the recent
important addition: touch [Bartolozzi et al., 2016], making it the ideal
platform to model the multimodal contributions to body representations.
Various surveys on body schema in robotics [Hoffmann et al., 2010],
exploration, body representations and internal simulation [Schillaci et al.,
2016], “synthetic psychology of the self” [Prescott and Camilleri, 2019] and
robot models to study the (en)active self [Lanillos et al., 2017, Nguyen et
al., 2021] have been published.

Remarkable demonstrations of this approach are the models of foetus
development from the laboratory of Yasuo Kuniyoshi at the University of Tokyo
(e.g., [Yamada et al., 2016]). They also illustate the potential of these
models as virtual experimental laboratories, showing for example the effect of
non-uniform versus uniform tactile sensor distribution [Mori and Kuniyoshi,
2010] or uterine versus extrauterine environment [Yamada et al., 2016].

Finally, it is important to note that not only is embodiment constitutive for
the nature and operation of body models in the brain (and hence humanoid
robots should be used to study body representations), but also, paraphrasing
Brooks [Brooks, 1991b] the body may sometimes be its own best model. In other
words, the physical body may be used directly, without having to have it
represented in the brain (see “Embodying the mind and representing the body”
[Alsmith and De Vignemont, 2012] or “body-in-the-brain versus body-in-the-
world” [Ataria et al., 2021, p. XV-XVI] for a deeper discussion). Thus, with
computational modeling only, one can hardly adopt other than the
representationalist approach to body maps [De Vignemont, 2018, Chapter 5].
Instead, with robots, the sensorimotor approach to body know-how (e.g.,
[Jacquey et al., 2020]) can also be modeled.

####  1.1.3 Abstracting general principles of learning body models and
applications

Apart from synthetic modeling of the mechanims of the “body in the brain”,
robots autonomously learning their body maps serve also abstracting general
principles and even applications (see Fig. 1.1 again for the different aspects
of the synthetic methodology). General characteristics of body models in
animals, humans, and robots are derived in [Hoffmann, 2021b], included in
Appendix LABEL:appendixB:body_models. The applications of robots learning
their body models are in automatic self-contained robot self-calibration and
they are reviewed in [Hoffmann, 2021a] and included in Appendix
LABEL:appendixC:self-calibration.

###  1.2 Body models in humans, animals, and robots

A theoretical account contrasting the body representations in the octopus,
humans, and robots is presented in [Hoffmann, 2021b], included in Appendix
LABEL:appendixB:body_models. On one hand, the example of the models of the
iCub robot (like forward and inverse kinematics, inverse dynamics, etc.) and
how it reaches for a tactile stimulus in [Roncone et al., 2014] serves as an
intuition pump to analyze body models in animals and humans. On the other
hand, what is known about how animals use and represent their bodies,
including the imporant aspect that behavior generation is in the brain-body-
environment coupling and hence not all features need to be modeled, is used to
inspire the design of future generations of machines that would inherit some
of the capacities demonstrated by animals like resilience.

###  1.3 Thesis overview

This thesis is a collection of published works, complemented by relatively
brief texts that explain the relationships between the individual articles.
The story of the thesis is largely told in three book chapters and the reader
is advised to consult them. In [Hoffmann and Pfeifer, 2018], Appendix
LABEL:appendixA:robots_for_embodied_cognition, the methodology of using
humanoid robots to study embodied cognition is explained and illustrated on
examples. In [Hoffmann, 2021b], Appendix LABEL:appendixB:body_models, body
models in humans, animals, and robots are contrasted. In [Hoffmann, 2021a],
Appendix LABEL:appendixC:self-calibration, an overview of biologically
inspired robot body models and self-calibration is provided for a robotics
encyclopedia. All three articles were prepared for a wider, often
interdisciplinary, audience, and they are thus accessible even for non-experts
in a specific field.

Figure 1.2: Habilitation Overview. Schematic visualization of the articles
composing the thesis, grouped according to common themes, and overlayed on the
synthetic methodology (analogous to Fig. 1.1). The articles in bold letters
are included in the thesis appendices.

The rest of the thesis brings together articles with a more technical content
and groups them as follows. In Chapter 2, an overview of related research on
the development of body representations in children is provided, along with my
own contributions through collaborations with developmental psychologists.
Chapter 3 describes biologically motivated case studies on robots learning
body models. Chapter 4 focuses on robots learning about the space surrounding
their bodies, so called peripersonal space. Chapter 5 deals with the topic of
automatic self-contained robot calibration. Chapter 6 brings together works on
safety of physical human-robot interaction. The thesis Appendix includes
thirteen peer-reviewed published works pertinent to the thesis topic. With one
exception [Roncone et al., 2016], I am either the first or the senior author
on all these publications—in the latter case, they originate from my research
group.

##  Chapter 2 Body knowledge in babies

A wealth of observations has been accumulated about development of the foetus
and the infant in the months before and after birth. There is certainly a
great deal to learn for the infant in this period, but the assumption in my
work is that the, possibly primary, target of what the infant is learning
about in this period is its own body. Understanding what its body parts are,
what it can do with them, and what the consequences would be, are essential
prerequisites for almost everything that follows. This is the sensorimotor or
ecological self [Neisser, 1988]. The second assumption relates to the way in
which the infant learns. In my work, self-touch or haptic exploration of one’s
own body is considered a key activity that may bootstrap learning about the
body as an object in space [Hoffmann, 2017]. We assume that motor-
proprioceptive-tactile contingencies provide sufficient material for the
infant to construct “functional body knowledge”, or body know-how, allowing
her for example to reach for specific body parts when presented with a tactile
stimulus. This sensorimotor account is presented in [Hoffmann et al., 2017]
and included in Appendix LABEL:appendixD:reaching_to_the_body. The role of
vision is increasingly important during postnatal development as the acuity
and gaze control improves. Eventually, it is likely that the representations
of the body and peripersonal space become predominantly vision-based. However,
to what extent, when, and how this occurs still needs to be established.

###  2.1 Spontaneous self-touch

111This section is adapted from Section II in [Gama et al., 2021].

Fetuses initially perform local movements directed to areas of the body most
sensitive to touch: the face, but also soles of feet [Piontelli, 2015, p.
113-114]. Later, from 26 to 28 weeks of gestational age, they also use the
back of the hands and touch other body areas like thighs, legs, and knees
[Piontelli, 2015, p. 29-30]. In addition, from 19 weeks, fetuses anticipate
hand-to-mouth movements [Myowa-Yamakoshi and Takeshita, 2006] (the mouth opens
prior to contact) and from 22 weeks, the movements seem to show the
recognizable form of intentional actions, with kinematic patterns that depend
on the goal of the action (toward the mouth vs. toward the eyes) [Zoia et al.,
2007].

Hand-mouth coordination continues to develop after birth [Rochat, 1993].
Specifically related to body exploration, [Rochat, 1998] writes: “By 2-3
months, infants engage in exploration of their own body as it moves and acts
in the environment. They babble and touch their own body, attracted and
actively involved in investigating the rich intermodal redundancies, temporal
contingencies, and spatial congruence of self-perception.” [DiMercurio et al.,
2018] followed spontaneous behavior of infants from 3 to 9 weeks; [Thomas et
al., 2015] from birth to 6 months of age. Their main findings regarding self-
touch were:

  * •

rostro-caudal progression: head and trunk contacts are more frequent in the
beginning, followed by more caudal body locations including hips, then legs,
and eventually the feet [Thomas et al., 2015]

  * •

contacts are typically made with the ipsilateral hand

  * •

_complex touches_ , as infants moved their hand while remaining in contact
with their body, were frequently observed by [DiMercurio et al., 2018]

In summary, infants acquire ample experience of touching their body, which
allows for the learning of the first tactile-proprioceptive-motor models of
the body. The ability to learn from this experience goes hand in hand with
dynamic neural development in this period [Tau and Peterson, 2010]; see
[Hoffmann, 2017] for a review focusing specifically on self-touch. Yet, the
behavioral organization of such early tactile exploration is not understood.
Are the touches on the body spontaneous or systematic? If there is a
particular structure—which seems to be the case [DiMercurio et al., 2018,
Thomas et al., 2015]—what drives this developmental progression? [Piaget,
1952] theorized that in newborns, action and perception as well as the
“spaces” of individual sensory modalities are separated (cf. [Van der Meer et
al., 1995] for evidence that visual and motor modalities are connected early
after birth). Until the connections are established, infants explore their
environment (and their body) randomly. [Piaget, 1952] also proposed a pivotal
role of repeated movements—primary circular reactions directed to learn
properties of the body and secondary circular reactions driven by the interest
on the effects they produce in the environment. However, to discriminate
spontaneous contacts from systematic (intrinsically motivated) exploration
remains a challenge.

###  2.2 Localizing touch on the body

222The first paragraph is adapted from Section II in [Gama et al., 2021].

A counterpart to recordings of spontaneous infant behavior is provided by
testing how they can reach to targets on their body. Lockman and colleagues
performed a series of studies [Chinn et al., 2019, Chinn et al., 2021,
Hoffmann et al., 2017, Leed et al., 2019, Somogyi et al., 2018] in which
vibrotactile targets (“buzzers”) were attached to infants’ body parts and
their ability and their way of reaching for the targets were analyzed. Targets
above the mouth and on the chin were successfully contacted already from 2
months of age [Chinn et al., 2021], followed by trunk area, legs, hands, other
areas on the face (forehead, ears), and elbows (around 9 months) (whole body –
pilot study [Hoffmann et al., 2017]; upper body [Leed et al., 2019]). For
targets on hands and arms, the arm with the buzzer and the contralateral arm
reaching for the target often moved simultaneously—the arm with the target
actively facilitating the removal [Chinn et al., 2019]. Regarding looking at
the target for locations where this was possible, the infants looked first in
86 of 189 trials (45.5%), reached first in 27 of 189 trials (14.29%), and
looked and reached simultaneously in 76 of 189 trials (40.21%). This last
strategy significantly increased with age (7 to 21 months).

Another window into the development of tactile localization is provided by
experiments that exploit crossed hands or crossed feet postures, often
employed to study conflicts between encoding touch in an anatomical versus
external frame of reference. In short, when one’s left hand is located in the
right peripersonal space, there is a conflict between where the hand is
normally and currently, which impairs tactile localization (one manifestation
is slower response in the temporal order judgement task – [Heed and Azañón,
2014] for a review). [Ali et al., 2015] tested two groups of infants—four-
month and six-month-olds—by applying tactile stimulation to their feet in a
crossed posture. Six-month-olds, like adults, showed a tactile localisation
deficit, indicating external spatial coding of touch; in striking contrast,
four-month-olds outperformed the older infants showing no crossed-feet
deficit, indicating that coding of touch in external space, called also
tactile remapping [Heed et al., 2015], develops at around 5 months after
birth, possibly in line with the growing importance of vision. In another
study, [Bremner et al., 2008] tested 6.5-month- and 10-month-olds in a crossed
and uncrossed hand posture and investigated their manual and looking
responses.

###  2.3 My contributions

The studies briefly reviewed above provide important constraints for the
embodied computational modeling work (Chapter 3). Some of the authors of the
works cited above have become my collaborators (Prof. Jeffrey J. Lockman,
Tulane University, USA; Prof. Daniela Corbetta, University of Tennessee,
Knoxville, USA; Dr. Kevin O’Regan, University Paris Descartes & CNRS, Paris,
France; Prof. Tobias Heed, Paris Lodron University Salzburg, Austria). I have
co-authored the following articles:

  * •

Chinn, L. K., Hoffmann, M., Leed, J. E., & Lockman, J. J. (2019). Reaching
with one arm to the other: coordinating touch, proprioception, and action
during infancy. Journal of experimental child psychology, 183, 19-32. [M.H.
author contribution 25%]

  * •

Chinn, L. K., Noonan, C. F., Hoffmann, M., & Lockman, J. J. (2019).
Development of infant reaching strategies to tactile targets on the face.
Frontiers in psychology, 10, 9. [M.H. author contribution 20%]

  * •

Somogyi, E., Jacquey, L., Heed, T., Hoffmann, M., Lockman, J. J., Granjon, L.,
Fagard, J., & O’Regan, J. K. (2018). Which limb is it? Responses to
vibrotactile stimulation in early infancy. British Journal of Developmental
Psychology, 36(3), 384-401. [M.H. author contribution 10%]

These articles are not included in this habilitation which is submitted for
the Technical Cybernetics program.

In addition, part II.C of [Hoffmann et al., 2017], Appendix
LABEL:appendixD:reaching_to_the_body, and part II of [Gama et al., 2021],
Appendix LABEL:appendixE:goal_directed contain also sections reporting results
of experiments with infants specifically targeting the needs of the modeling
endeavour.

###  2.4 Summary

The observations from the studies on babies provide inputs and constraints for
the embodied computational modeling. In line with the synthetic methodology
(Section 1.1), inspiration from biology may contribute to the discovery of
general principles that may eventually give rise to useful technology like
self-calibrating robots. However, the primary goal is to help uncover the
mechanims of the development of body know-how in infants. Collecting
unambiguous empirical evidence is complicated by the age of the
subjects—infants in the first year are preverbal and one thus cannot easily
instruct them and their cooperation during experiments is limited. Similarly,
manipulations (e.g., reaching for a tactile stimulus only with a certain limb)
are hard to orchestrate and additional setups like motion capture or brain
imaging are also almost impossible to arrange. Therefore, robot models that
allow arbitrary manipulations and where all internal variables can be accessed
provide an indispensable tool.

##  Chapter 3 Robots learning body models

The results of empirical studies from Chapter 2 flow into embodied
computational models on humanoid robots that address the mechanisms of
development of reaching and somatosensory perception in early infancy.
Humanoid robots with pressure-sensitive electronic skins covering large areas
of their bodies provide the right platform for this type of work. There are
four articles in this research strand [Hoffmann et al., 2017, Gama et al.,
2021, Hoffmann et al., 2018, Nguyen et al., 2019], three of which are included
in the thesis.

The article [Hoffmann et al., 2017], included in Appendix
LABEL:appendixD:reaching_to_the_body, is a conceptual one, in collaboration
with developmental and cognitive psychologists and provides a natural
transition from the empirical studies to embodied computational modeling of
the development of reaching to the body.

The second article [Gama et al., 2021], included in Appendix
LABEL:appendixE:goal_directed, focuses on active body exploration and compares
different algorithms from the family of intrinsically motivated learning (or
artificial curiosity) [Schmidhuber, 1991, Oudeyer and Kaplan, 2007, Baranes
and Oudeyer, 2013, Baldassarre and Mirolli, 2013]. This work demonstrates that
efficient exploration of the skin space and learning of inverse models is
possible. First grounding of the modeling work in experimental data is
attempted (study of motor redundancy in infant reaching and link to “complex
touches” from [DiMercurio et al., 2018]).

The third article [Hoffmann et al., 2018], included in Appendix
LABEL:appendixF:robot_homunculus, studies the development of a biologically
motivated representation of the robot skin surface: the robot “tactile
homunculus”. The robot is exposed to tactile stimulations on its whole body
and the corresponding tactile activations are recorded. These are then fed
into a self-organizing (or Kohonen) map algorithm and the representations that
emerge are studied. Modifications of the standard algorithm that provide the
right constraints to channel learning toward the layout of the neural map
observed in primate brains are developed.

The fourth article [Nguyen et al., 2019] studies the development of reaching
to objects external to the body. These are perceived visually, but we
concentrate on the role of haptic feedback in learning the behavior on the
robot. If the object is at first randomly contacted, proprioception provides
an alternative to vision to guide subsequent reaching movements. Such
“somatosensory coding of space” connects reaching to the body with reaching to
external objects.

##  Chapter 4 Robots learning peripersonal space representations

Next to “body space” or “personal space”, the space immediately surrounding
the body is called peripersonal space. There are two related but different
meanings associated with peripersonal space (PPS): (i) space immediately
surrounding the body and (ii) space that we can act upon / within our reach.
Their representations in the brain are realized by fronto-parietal networks,
with an important role attributed to bimodal neurons with visuo-tactile
receptive fields (RFs) [Cléry et al., 2015]. The first notion, space
surrounding the body, can be pictured as a “bubble” around individual body
parts and following those body parts in space. It is realized by bimodal
neurons with tactile RFs on the skin and visual RFs around. The visual
responses appear to be tuned to dynamically approaching objects and their
activation is thus anticipatory, predictive of touch in the corresponding skin
area. There are also behavioral responses associated with the stimulation of
some neurons of this network (squinting, ducking, and withdrawing from the
direction of the potential threat). Therefore, this circuit is thought to be
responsible for self-defense and maintaining a safety margin around the body
[Graziano and Cooke, 2006]. Little is known about the development of
peripersonal space representations, with some evidence suggesting that even
newborns can make sense of multisensory (audio-visual in this case) cue
combinations specifying motion with respect to themselves [Orioli et al.,
2018].

Taking advantage of humanoid robots with whole-body sensitive skin, we
designed a method where the robot learns such a safety margin from experience.
The robot records trajectories of approaching objects and if they eventually
contact its body as perceived by the artificial skin, it learns the likelihood
of such future contact. The objects’ positions perceived by the robot by
stereo vision are remapped into the reference frames of every tactile sensor
(taxel). Every taxel then continuously updates a representation of the
probability of objects at a certain distance [Roncone et al., 2015] or with a
certain time to contact [Roncone et al., 2016] colliding with it. These
“threatened” taxels can be aggregated and a most likely future collision site
on the robot body computed. This location on the robot body can then be
connected to a controller that avoids the collision by moving the exposed body
part away (video: https://youtu.be/3IaXxNwC_7E). Alternatively, using the
opposite sign for the movement direction, “whole-body reaching/catching” can
be easily realized. The work [Roncone et al., 2016] is included in Appendix
LABEL:appendixG:pps.

We developed an alternative learning algorithm in [Straka and Hoffmann, 2017],
a neural network composed of a Restricted Boltzmann Machine and a feedforward
neural network. The former learns in an unsupervised manner to represent
position and velocity features of the stimulus. The latter is trained in a
supervised way to predict the position of touch (contact). Compared to
[Roncone et al., 2016], this model was trained in a simulated environment but
it importantly take into account also the the uncertainty of all variables.

Apart from modeling the operation, development, and adaption of peripersonal
space representations in the brain, “perirobot space” representations are key
for safe human-robot interaction. Appropriate collision avoidance strategies
cannot be selected without a representation of the space surrounding the
robot. This space can be represented in various forms with regards to the
needs of the application or robot platform. Multiple questions come forth in
this context, namely the way the space is structured, how it will dynamically
adapt and what is its geometry. A key component is the form of the
representation of the robot and human body parts, or, in general, the
representation of obstacles. Drawing on the results of the computer graphics
community (see [Jiménez et al., 2001] for a survey), this often takes the form
of some collision primitives. These can be simple shapes like spheres [Flacco
et al., 2012] or more complex meshes [Polverini et al., 2017] and can differ
for the robot and the human. [Zanchettin et al., 2015] represent robot links
as segments and humans as a set of capsules. These shapes can also have a
temporal aspect and represent so-called swept volumes, i.e. zones where the
human or robot moved. For safety to be guaranteed, the whole body of both
agents should be represented and considering only the robot end-effector or
human hands or head does not suffice. Some representations change the volume
dynamically based on the robot or human velocity [Polverini et al., 2017,
Zanchettin et al., 2015, Lacevic and Rocco, 2010, Magnanimo et al., 2016]. The
technologies applied to perception can influence the structure of the
perirobot space too: Euclidean space can be replaced by a depth space approach
to account for the occlusions and specific geometries of the field of view of
a RGB-D sensor [Flacco et al., 2012, Flacco et al., 2015]. Examples are
depicted in Fig. 4.1. The best representation is yet to be found. These
considerations tie directly into the topic of safe physical human-robot
interaction discussed in Chapter 6 and our publication [Svarny et al., 2019].

Figure 4.1: Perirobot space representations. (A) Depth space approach [Flacco
et al., 2012]. (B) Kinetostatic danger field [Lacevic and Rocco, 2010]. (C)
Dynamic safety fields [Magnanimo et al., 2016].

##  Chapter 5 Robot self-calibration

As briefly reviewed in Chapter 2, newborns have not only limited conceptual
and spatial knowledge about their bodies, but also limited “body know-
how”—means to use their bodies for purposeful action or to localize and act on
stimuli on their body. They learn the necessary body models in the first years
after birth, while continuously incorporating physical body growth as well as
maturation of the sensory apparatus (vision in particular). Next to the
developmental time scale, human body representations were found to be adaptive
(called plastic in neuroscience) on much shorter time scales, as demonstrated
by the ability to use tools and their incorporation into the body schema
[Maravita and Iriki, 2004], for example. Both capacities—learning and quickly
adapting body models—would be highly desired in robots.

Nowadays, humanoid but also other robots come with a rich set of powerful yet
inexpensive sensors like cameras, RGB-D cameras, inertial, tactile or force
sensors. This opens up the possibility for calibration approaches that are
more self-contained, can be performed autonomously and repeatedly by the
robot, and that simultaneously estimate the position of the sensors with
respect to the robot. The key to self-calibration is redundancy. The kinematic
chain can be closed exploiting physical contact (aka closed-loop calibration
approaches) or by observing the robot pose using visual sensors (open-loop
calibration approaches) (see [Hollerbach et al., 2016]). Fig. 5.1 provides an
overview. Next to traditional methods exploiting contact with the environment
(e.g. robot touching a planar surface – Fig. 5.1B) or external metrology
systems (e.g. laser trackers – Fig. 5.1D), we studied self-contact (Fig. 5.1A)
and self-observation (Fig. 5.1C) as methods that are suited for automatic
self-contained calibration.

Figure 5.1: Schematics of calibration using self-contact (A), contact with a
plane (B), self-observation (C), and external device (D). Figure and caption
from [Rozlivek et al., 2021].

An overview of the state of the art and future research directions regarding
biologically inspired robot body models and self-calibration is provided in
[Hoffmann, 2021a], included in Appendix LABEL:appendixC:self-calibration. In
addition, four case studies are also included in this thesis.

In [Stepanova et al., 2019], Appendix LABEL:appendixH:iCub_selfcalib, we
systematically studied on the simulated iCub humanoid robot how self-
observation, self-contact, and their combination can be used for self-
calibration. We found that employing multiple kinematic chains (self-
observation and self-touch) is superior in terms of optimization results as
well as observability.

In [Rozlivek et al., 2021], Appendix LABEL:appendixI:multirobot, we provided a
unified formulation that makes it possible to combine traditional approaches
with self-contained calibration available to humanoid robots in a single
framework and single cost function. Second, we presented an open source
toolbox for Matlab (https://github.com/ctu-vras/multirobot-calibration) that
provides this functionality, along with additional tools for preprocessing
(e.g., dataset visualization) and evaluation (e.g.,
observability/identifiability).

In [Rustler et al., 2021], Appendix LABEL:appendixJ:nao_skin_calib, we used
self-contact as one of the methods to calibrate the positions of 970 pressure
sensors on the body of a humanoid robot (the opposite approach to [Roncone et
al., 2014] where the skin was used to improve kinematic calibration). We
experimentally compared the accuracy and effort associated with the following
skin spatial calibration approaches and their combinations: (i) combining CAD
models and skin layout in 2D, (ii) 3D reconstruction from images, (iii) using
robot kinematics to calibrate skin by self-contact.

Finally, in [Stepanova et al., 2022], Appendix LABEL:appendixK:motoman, all
four calibration methods schematically illustrated in Fig. 5.1—self-contact,
contact with a plane, self-observation, and external device—were
experimentally compared on a dual-arm industrial manipulator. The main
findings were: (1) when applying the complementary calibration approaches in
isolation, the self-contact approach yields the best and most stable results;
(2) all combinations of more than one approach were always superior to using
any single approach in terms of calibration errors and the observability of
the estimated parameters. Combining more approaches delivers robot parameters
that better generalize to the workspace parts not used for the calibration.

##  Chapter 6 Safe physical human-robot interaction and collaborative robots

Robots are leaving safety fences and start to share workspaces or even living
spaces with humans. As they leave controlled environments and enter domains
that are far less structured, they need to dynamically adapt to unpredictable
interactions and guarantee safety at every moment. There has been rapid
development in this regard in the last decade, with revisions of existing and
introduction of new safety standards ([ISO 10218, 2011, ISO 13855, 2010,
ISO/TS 15066, 2016]; see e.g. [Haddadin and Croft, 2016] for a survey)) and a
rapidly growing market of collaborative robots. According to [ISO/TS 15066,
2016], there are two ways of satisfying the safety requirements when a human
physically collaborates with a robot: (i) Power and Force Limiting (PFL) and
(ii) Speed and Separation Monitoring (SSM).

For PFL, physical contacts with a moving robot are allowed but the forces /
pressures / energy absorbed during a collision need to be within human body
part specific limits. This translates onto lightweight structure, soft padding
and no pinch points on the robot side, in combination with collision detection
and response relying on motor load measurements, force/torque or joint torque
sensing. This is addressed by interaction control methods for this post-impact
phase (e.g., [De Luca et al., 2006, Haddadin et al., 2008]; [Haddadin et al.,
2017] for a survey). The performance of robots complying with this safety
requirement in terms of payload, speed, and repeatability is limited.

Safe collaborative operation according to speed and separation monitoring
prohibits contacts with a moving robot and thus focuses on the pre-impact
phase: a protective separation distance, $S_{p}$, between the operator and
robot needs to be maintained at all times. When the distance decreases below
$S_{p}$, the robot is commanded to halt. In industry, $S_{p}$ is typically
safeguarded using light curtains (essentially electronic versions of physical
fences) or safety-rated scanners that monitor 2D or 3D zones. However, the
flexibility of such setups is limited—the information is reduced to detecting
whether an object of a certain minimum volume has entered a predefined zone.
The higher the robot kinetic energy, the bigger is its footprint on the
factory floor.

This topic ties with the rest of the thesis as follows. First, not only robot
performance but also safety is dependent on its accuracy and calibration.
Thus, machines learning and adapting their body models (Chapter 3 or self-
calibrating robots in Chapter 5) are also likely to be safer for their
environments. Second, the use of artificial sensitive skins researched in my
group can contribute an additional protective layer for collision detection
and isolation during physical HRI (the PFL regime in particular). Third,
adaptive multimodal representations of the robot peripersonal space (Chapter
4) could give rise to robots with a human-like margin of safety around their
bodies and to new solutions to robot safety in the SSM regime.

In [Svarny et al., 2019], Appendix LABEL:appendixL:SSM_PFL, we deployed the
two collaborative regimes (PFL and SSM) in a single application and studied
the performance in a mock collaborative task under the individual regimes,
including transitions between them. Additionally, we compared the performance
under “safety zone monitoring” with keypoint pair-wise separation distance
assessment relying on an RGB-D sensor and skeleton extraction algorithm to
track human body parts in the workspace. Best performance has been achieved in
the following setting: robot operates at full speed until a distance threshold
between any robot and human body part is crossed; then, reduced robot speed
per power and force limiting is triggered. Robot is halted only when the
operator’s head crosses a predefined distance from selected robot parts.

In [Svarny et al., 2021], Appendix LABEL:appendixM:3dCFM, we measured the
forces exerted by two collaborative manipulators moving downward against an
impact measuring device. First, we empirically showed that the impact forces
can vary by more than 100 percent within the robot workspace. The forces are
negatively correlated with the distance from the robot base and the height in
the workspace. Second, we presented a data-driven model, 3D Collision-Force-
Map, predicting impact forces from distance, height, and velocity and
demonstrate that it can be trained on a limited number of data points. Third,
we analyzed the force evolution upon impact and found that clamping never
occurs for one of the robots (UR10e). We showed that formulas relating robot
mass, velocity, and impact forces from ISO/TS 15066 [ISO/TS 15066, 2016] are
insufficient—leading both to significant underestimation and overestimation
and thus to unnecessarily long cycle times or even dangerous applications. We
proposed an empirical method that can be deployed to quickly determine the
optimal speed and position where a task can be safely performed with maximum
efficiency.

Finally, in collaboration with the group of Sami Haddadin at Technical
University of Munich, we investigated how the velocity of a collaborative
manipulator can be modulated by monitoring the interaction with a human. The
details are in [Zardykhan et al., 2019] (not included in Appendix).

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