[robotics-worldwide] [journals] CFP: Introspective Methods for Reliable Autonomy

Tomasz Kucner Tomasz.Kucner at oru.se
Wed Sep 27 13:35:27 PDT 2017


IEEE Transactions on Cognitive and Developmental Systems

Special issue on:

"Introspective Methods for Reliable Autonomy"

Submission deadline: 2018.01.31

As humans, understanding our own limitations, failures and
shortcomings is a key for improvement and development. This
knowledge is crucial for altering our behaviors, e.g. to execute
tasks in a more cautious way. Correspondingly, equipping robots
with a set of skills that allows them to assess the quality of
their sensory data, internal models, used methods etc. will
greatly improve their overall performance.

The problem of introspection, directly or indirectly, relates to other
research topics: planning, execution monitoring, active perception
and mapping. Accordingly, an improved understanding of
introspection in robotics has a direct impact on a large variety
of application areas (e.g. search and rescue, intralogistic,
assistive robotics).

The introspection impacts the most the following aspects of robotics
system: safety, reliability and the maintenance costs.

Information on the internal state of the robot is crucial to make
decisions if it is safe to execute the assigned mission
considering not only the current state of the perceived
environment, but also the internal state of the robot.

Continuous monitoring of the internal state of the robot and automatic
assessment can be also used to enhance the maintenance
process. Information about the internal state of the robot can be
used to estimate the likelihood of potential failure and tailor
the efforts to prevent it or to speed up the recovery or repair
process by providing detailed information to a human operator or
even enable self-repair.

Introspection takes active role in the process of preventing of
malfunctions of the robotic system and help to speed the repair
process up. These two features have direct impact on the running
cost of a robotic system. Preventing unplanned interruptions in
the robot operation and shortening the time of the planned
interruptions has a direct impact on the cost of robot

It is also important to remember that introspective information is a
cornerstone of all methods aiming to robotics self-improvement. It
provides information crucial in the learning and development
process. In this context, it is possible to draw a parallel
between human and robotic system. Assessment of the internal state
is important input helping to anticipate if the planned action is
feasible for the agent (either human or robot). For a complex
system, it is difficult to perform such assessment relying only on
predefined set of rules and conditions.

Therefore, it is necessary to use learning algorithms which will be
able to connect the preexisting internal and external conditions
with the outcome of a planned action. In such a configuration, a
failure became a crucial element of a learning process of an
autonomous system.

Finally, it is important to emphasis that introspection is a topic
which spans across multiple fields. The introspection is
originally a human ability. It is recent years when the idea of
introspection is also becoming present in the field of robotics.

Therefore, to obtain a complete picture of the problem of
introspection in autonomous systems it is important to have a
closer look also at psychological aspect of
introspection. Moreover, the impact of introspection in the
context of the cognitive science cannot be overlooked.

The primary topic of this issue is to present work on:

How to assess the quality of internal models, methods, sensory data
and the hardware used by robots and how to alter their behavior
using this information?

The aim of this special issue is fourfold:

* Survey the state of the art in the field.
* Define open research questions in the field.
* Provide a venue to present the recent developments in the field of
* Present system papers showing how introspection is integrated and
affects performance of a system.


This special issue is addressed to researchers interested in
the development of introspective methods for robust autonomy
across different research areas. We expect to receive
submissions relevant for following research fields, but to
name a few: Long term autonomy, safe operation of robots under
uncertanity, performance awareness, reliable-aware operation,
cooperative robotics, cognitive and learning robots,
developmental robotics, Human-Robot Interaction. Introspection
is broad term covering a set of topics. Topics relevant to
this special issue include, but are not limited to:

* Internal assessment (Map quality assessment, Perception quality
assessment, Classification quality assessment)
* Analysis (Failure analysis, Execution monitoring, Meta-reasoning)
* Introspection-related actions (Failure recovery, Reconfigurable
robots, Planning with uncertainty)


2018.01.31 - Deadline for manuscript submissions.
2018.05.15 - Notification of authors
2018.06.15 - Deadline for submission of revised manuscripts
2018.07.31 - Final decisions


Manuscripts should be prepared according to the “Information
for Authors” of the journal found at https://urldefense.proofpoint.com/v2/url?u=http-3A__goo.gl_0eMHUd&d=DwIF-g&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=iE3H5ujoBBcHJK01NQPUMaXD1WMWvsNmKDewykXAV1w&s=yoFsVk59xw5a1mYbVIwdiybvS7-guh80c74lOMwzrSM&e= and
submissions should be made through the IEEE TCDS Manuscript
center at https://urldefense.proofpoint.com/v2/url?u=https-3A__mc.manuscriptcentral.com_tcds-2Dieee&d=DwIF-g&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=iE3H5ujoBBcHJK01NQPUMaXD1WMWvsNmKDewykXAV1w&s=Qan0B66-Ngkd_ZSF_vcG1Ym2M4VE5cPGCzky0nbUEB8&e= selecting
the category “SI: Introspective Methods for Reliable


The IEEE Transactions on Cognitive and Developmental Systems
(TCDS) focuses on advances in the study of development and
cognition in natural (humans, animals) and artificial (robots,
agents) systems. It welcomes contributions from multiple
related disciplines including cognitive systems, cognitive
robotics, developmental and epigenetic robotics, autonomous
and evolutionary robotics, social structures, multi-agent and
artificial life systems, computational neuroscience, and
developmental psychology. Articles on theoretical,
computational, application-oriented, and experimental studies
as well as reviews in these areas are considered.


Yaochu Jin
Department of Computer Science
University of Surrey
Surrey, United Kingdom


Tomasz Piotr Kucner
Centre for Applied Autonomous Sensor Systems
Örebro University
Örebro, Sweden
tomasz.kucner at oru.se<mailto:tomasz.kucner at oru.se>

Prof. Sören Schwertfeger
School of Information Science and Technology
ShanghaiTech University
Shanghai, China
soerensch at shanghaitech.edu.cn<mailto:soerensch at shanghaitech.edu.cn>

Martin Magnusson
Centre for Applied Autonomous Sensor Systems
Örebro University
Örebro, Sweden
martin.magnusson at oru.se<mailto:martin.magnusson at oru.se>

Achim J. Lilienthal
Centre for Applied Autonomous Sensor Systems
Örebro University
Örebro, Sweden
achim.lilienthal at oru.se<mailto:achim.lilienthal at oru.se>

Rudolph Triebel
Institute of Robotics and Mechatronics
German Aerospace Center
Oberpfaffenhofen-Weßling, Germany
rudolph.triebel at dlr.de<mailto:rudolph.triebel at dlr.de>

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