# Potentials and Limitations of Deep Neural Networks for Cognitive Robots

Doreen Jirak and Stefan Wermter
Knowledge Technology, Department of Informatics, University of Hamburg,
Germany

###### Abstract

Although Deep Neural Networks reached remark- able performance on several
benchmarks and even gained scien- tific publicity, they are not able to
address the concept of cognition as a whole. In this paper, we argue that
those architectures are potentially interesting for cognitive robots regarding
their perceptual representation power for audio and vision data. We identify
crucial settings for cognitive robotics where deep neural networks have as yet
only contributed little compared to the challenges in this area. Finally, we
highlight the rather unexplored area of Reservoir Computing for sequence
learning. This new paradigm of learning recurrent neural networks in a fast
and robust way qualifies to be an integral part of cognitive robots and may
inspire novel developments.

##  I Introduction

With the advent of both data availability and accelerated GPU computations,
deep neural network architectures (DNN) have flourished over the past years
and showed tremen- dous success on several benchmarks for computer vision, and
audio and speech. However, DNN models demand high training times for sensitive
parameter tuning and network- specific optimization like regularization
techniques, which is a rather engineering approach. Although DNNs are
motivated by neurobiological principles observed in the visual cortex, the
complete picture of the computations boils down to learning filters detecting
specific frequency characteristics like edges in images or voice pitch in
audio data. As the learning is carried out in a supervised manner, the use of
a huge amount of labeled data has a crucial impact on high accuracy and other
evaluation measures. The area of cognitive robotics is highly
interdisciplinary and advances in sensor and robot technology allow the in-
tegration of human-like capabilities. The aim of cognitive skills in robots is
motivated by their increased integration in human’s everyday life, whether as
a companion in human- robot interaction (HRI) or as an autonomous system in
health care and industry. DNNs can only address part of what actually
cognitive systems need and which challenges they face. The focus on supervised
benchmarking shifts research on the multiple, interconnected facets of
cognitive robots to a rather engineered approach accompanied by constraints on
the data and the environment. Critical investigations on DNNs also reveal that
due to their high dependence on apriori knowledge, they are easily fooled [1]
and fail to cope with, e.g., real-world speech data from children. Recent
advances of (deep) unsupervised networks closely related to neural information
principles like sparse autoencoders or self organizing maps may stimulate
again joining state-of-the-art algorithms successfully applied to robot
scenarios with deep architectures. In the present paper, we will discuss the
pros and cons of DNNs in cognitive robotics. We highlight their usefulness but
also limitations, discussing also the potential of Reservoir Computing in the
context of cognitive robots.

##  II Lab Conditions vs. the Real World

A challenging aspect of cognitive systems and specifically cognitive robots is
the high parallelization of tasks with a vast amount of incoming stimuli which
need to be filtered to trigger a suitable (re)action. We are able to focus our
attention onto a specific object or sound but we can also be easily distracted
as is observable in the ventriloquist effect, where humans’ sound perception
is influenced by a doll being an additional visual stimulus. Researchers thus
developed algorithms to capture the most salient cues in the scene but either
on datasets [2] or under very constrained lab conditions [3]. Although imple-
mentations using deep networks showed promising results in those limited
cases, DNN will have problems with changing conditions in real-world
environments due to retraining issues inherent in closed set classification
system. A recent robotic experiment on indoor exploration proposed a framework
based on DNN to exploit the robot capabilities to learn the unknown terrain in
a human-like fashion using the turtlebot platform [4]. For the robot vision, a
Kinect device was mounted delivering depth information. Although one may argue
that Kinect sensors are error-prone and question their reliability in a robust
image capturing, the availability of the authors’ code enable other
researchers to explore further the advantages and pitfalls of the approach,
identifying further problems and possible solutions enriching discussions in
the cognitive robotic community for this important task. Another important
factor is that learning in humans is not subject to one specific task for a
specific point in time and space but is continuous throughout life. Although
DNNs produce stable feature representations which may code for more abstract
concepts similar to neural codes along the brain hierarchy, yet they are less
flexible and do not allow online adaptation nor any memory mechanisms. A new
trend, how- ever, to avoid retraining is using transfer learning: weights of a
deep network successfully trained on a set of e.g. images are transfered to
solve a similar task. A fruitful combination for robot applications could be
to join those strategies with open set classification algorithms like the
growing-when-required networks (GWR, [5]), which allow insertion and deletion
of labels. The benefit of a GWR is that it provides a computational method to
the question how actually learning is guided in the absence of any labels. The
robust representations emerging from a deep neural network can thus complement
the flex- ible classification in environments beyond the restricted lab
conditions.

##  III Children vs. Adult Learning

Developmental psychology is an important research area to gain an
understanding on how we acquire cognitive skills starting from first imitation
tasks to controlled motor acts, from first babbling to words and sentences to
finally reasoning, consciousness and empathy (theory of mind). In contrast to
deep networks being trained on billions of e.g. images for object recognition,
infants learn rather slow and are provided only with a very limited excerpt of
the world. DNNs tuned for benchmarking as was carried out during the past
years is very different from developmental learning and the ac- quisition of
cognitive skills. Although their performance on speech recognition tasks is
indubitable, DNN models show low performance when the speech resources are
getting noisy (e.g. repetitions) or are from children, since their language
differs from adults. In addition, the way children learn is driven by high
intrinsic motivation [6], curiosity [7] and their physical interaction with
the world (embodiment). The latter was shown to be essential in exploring
affordances for the associative learning between objects and grasp types [8],
keeping also into account parental scaffolding [9]. The link between motor
activation in the brain and language acquisition speaks in favor of embodied
learning which demands to ground experiments in real robots (see e.g. [10])
rather than a set of labeled data. Until now, these important aspects to
acquire cognitive skills are absent in learning systems based on DNNs which
may be explained by their difficulty in interpretation. A literature research
on this topic revealed that only a few trends emerged towards closing this
gap. One approach used a generative deep neural network based on the time
course of learning object categories in children [11]. Essentially, the
authors demonstrated that despite using linear neurons, their model was able
to capture the refinement of semantic labels and with an additional analysis
on the temporal dynamics of gradient- based learning, that this
differentiation emerges naturally in hierarchical networks. Another recent
paper [12] identified a gap between de- velopmental learning and deep neural
networks along the hierarchical processing of object recognition on the
perceptual level for the acquisition of action knowledge on the cognitive
level where affordances play a crucial role. In the light of embodied
cognition, affordances are grounded in sensorimotor experiences. The authors
[12] argued in favor of unsupervised learning techniques as labels of
sensorimotor experiences do not exist. Though deep networks do not allow for
flexible, online adaptation of e.g. grasping when affordances may change
(think about the change of perspective of a mug handle), the main argument
integrating deep unsupervised models is that they can serve as an enhancement
strategy for learned sensorimotor experience as in humans during sleep. In
conclusion, although still other developmental learning mechanisms as the
mentioned curiosity are missing and can not be captured by DNNs, they might be
a useful tool for representational learning, encouraging a novel research area
called ”deep developmental learning” [12]. Regarding the interpretability
issues in DNN, a deep net- work in a one-shot word learning scenario was
employed and the results investigated by means of methods from cognitive
psychology, namely the ”cognitive bias” which allows children to eliminate
word hypotheses from a vast possible word space [13]. The network evaluation,
which used training on the popular ImageNet database, underpinned the “shape
bias” hypothesis, i.e. that humans tend to label objects similar in shape as
one entity. Joining DNNs with cognitive psychology may open the way to further
investigate the positive as well as negative aspects of biases in human
learning, complementing behavioral studies which found the basis for many
cognitive robotic scenarios.

##  IV Reservoir Computing for Sequential Learning in Robot Scenarios

Learning sequential tasks is inevitably necessary for the development of
cognitive systems, may it be navigation, planning or communication with
language and gestures. Deep neural architectures do not possess an explicit
time resolution to capture time correlation nor timescales inherent in
sentences and actions. A popular technique to overcome the missing temporal
link in, e.g., CNNs is to use 3D convolution kernels for image stacks
representing videos. However, the benefit of such kernels on the performance
compared to the standard 2D kernel is not yet satisfiable answered [14]. Due
to the inherent correlations in sequential tasks, Re- current Neural Networks
(RNN) are a widely used tool as they provide local network memory and are thus
able to compute different time aspects (e.g. continuous-time RNN). RNNs in
their diverse implementations were successfully used in cognitive robotic
scenarios (see e.g. [15], [16]). Due to the error computations propagated
through the whole network using gradient descent, RNN training suffers from
vanishing or exploding gradients, trapping into local minima, and network
bifurcations. To overcome these issues, RNNs need a lot of retraining and are
thus computationally demanding. Alternatively, the Reservoir Computing (RC)
paradigm in- troduced a simplified training by the conceptual separation of a
high-dimensional reservoir of randomly connected neurons providing rich input
representations which are read out by simple linear models and thus updating
the network weights becomes obsolete. Popular implementations are the Liquid
State Machines [17] (LSM) and Echo State Networks [18] (ESN) the latter
demonstrating competitive performance in predictions tasks for (chaotic)
timeseries [19]. Despite the reduced computational effort, only a few
applications for robots were developed until today. Some highlights in the
literature using the RC principles include navigation [20], [21] and language
acquisition [22]. Especially the latter research shows that RC is a promising
framework combining principles from neuroscience and developmental psychology
for the highly complex cognitive task of learning and understanding language.
Also, only little is known about the potential of RC algorithms for visual
tasks. As images and videos are highly complex in structure and introduce
varying lighting conditions and perspectives, a solution combining the robust
feature capabilities provided by DNNs with ESNs capturing the inherent
temporal structure showed good performance on a gesture recognition task for a
set of command gestures [23]. The coupling of DNNs with RC was also
highlighted recently regarding future research directions [24]. Methods based
on RC can also be exploited for robot control [25] and locomotion [26] but
coupling RC with more complex robot scenarios is still in its infancy. We
believe, that the area of cognitive robotics would sub- stantially benefit
from further research on the potentials of RC frameworks complementing other
computational methods like DNNs for robot perception or even substituting
existing RNN methods exploiting the simplified yet robust training. Also,
analysis of ESNs regarding their ”edge of stability” would unify hypotheses on
cognition underlying chaotic computa- tions with performance maximization
[27], which can lead to effective network design.

##  V Conclusion

The argument in favor of cognitive systems is diametral to the current
benchmarking and competition of accuracies using deep neural networks.
Although those network architectures are beneficial for perceptual or
supervised learning involved in the acquisition of cognitive skills, we
highlighted some limitations and suggest alternative computations for future
ap- plications. This brief communication focused on recent neural network
approaches and Reservoir Computing to stimulate discussions on the integration
of those methods into cognitive robots. Our intention was not to exclude other
important methods like probabilistic models or dynamic neural fields. We
rather argue, that advances in technology in humans everyday life demand
tighter coupling in the community regarding current progress in the neural
network and machine learning area with cognitive models proven to be
successful in the development of cognitive robots.

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