ISSN 2186-7437

NII Shonan Meeting Report

No. 2013-14

Cognitive Social Robotics: Intelligencebased on Embodied Experience andSocial Interaction
Tetsunari Inamura
Tamim Asfour
Sethu Vijayakumar
November 11 - 14, 2013

National Institute of Informatics
2-1-2 Hitotsubashi, Chiyoda-Ku, Tokyo, Japan

Cognitive Social Robotics: Intelligence based on
Embodied Experience and Social Interaction
Organizers:
Tetsunari Inamura (National Institute of Informatics, Japan)
Tamim Asfour (Karlsruhe Institute of Technology, Germany)
Sethu Vijayakumar (The University of Edinburgh, United Kingdom)
November 11 - 14, 2013
Understanding mechanisms underlying intelligence of human beings and animals is one of the key approaches towards developing intelligent robot systems.
Since the mechanisms of such real-life intelligent systems are so complex, physical interactions among agents and their environment and the social interactions
among agents should be considered. On the other hand, the interactions amongrobots, autonomous systems, their environments and people at various scales
present some of the most sophisticated scientific challenges we must solve torealize the next generation of robots. Comprehension and knowledge in many
related fields such as cognitive science, developmental psychology, brain science,evolutionary biology, and robot engineering is also required - making a case forstrong interdisciplinary interaction of minds for implementing this approach. In
this decade, an academic field named cognitive developmental robotics has beenformalised with these aims. This approach focuses on embodied intelligence
which is one of the deficient points in GOFAI (Good Old Fashioned Artificial
Intelligence). While huge strides are being made in cognitive robotics, the quality and performance of the robots’ intelligence has not matched the versatilityof human counterparts. One of the reasons for this deficiency is that perhaps
we do not yet fully exploit the interactions and embodiment, both in the physical and social (and virtual) domain. One of the diﬃculties is the huge cost ofcollecting embodied and social experience due to limitation of robots’ operatingtime, robustness and limited opportunity of social interaction with humans due
to limits in deployment. A promising direction is to accelerate the developmentof intelligence from large-scale long-term, persistent embodied experience weterm ’big social experiences’ - addressing many of the issues analogous to the’Big Data’ challenges in Informatics.
This Shonan meeting focuses on synthetic research in cognitive social robots,building on the premise that intelligence develops based on physical embodied
interaction between body and environment, social interaction between agentsand human, with the aim to address both the following aspects:
1. Exploiting Physical Embodiment and Interaction: Actuators, Control,
Multi-contact Planning, Natural Dynamics and Design
2. Networked Social Intelligence: Networked interactions, Multi-agent systems, Software for connectivity, Emergent Capabilities
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To foster this, we aim for interdisciplinary discussions with wide viewpoint fromvarious research fields such as cognitive science, developmental psychology, brain
science, and not just robotics. The scope of this meeting will include humanrobot interaction, machine learning, cognitive science, simulation technology
for long-term large scale interaction, learning by demonstration, human biomechanics, multimodal sensory experience (auditory, speech, gestures). One of thetarget applications as material for the discussion would be acquisition of knowledge and skills from natural interaction between embodied agents and humanbeings; however, this meeting would like to touch on other future big challenges
related to the cognitive social robotics with a broad perspective.
The following three sessions are assigned to discuss emergent Intelligence
which covers from sensorimotor experience to symbols and language.
• Embodied Experience and Interaction• Biologically Inspired Design• Networked Social Intelligence2Participants• Rüdiger Dillmann, Karlsruhe Institute of Technology, Germany• Tomohiro Shibata, Nara Institute of Science and Technology, Japan• Jun Morimoto, Advanced Telecommunications Research Institute International(ATR), Japan• Sethu Vijayakumar, The University of Edinburgh, United Kingdom• Madhusudhan M. Venkadesan, National Centre for Biological Sciences,India• Neelima Sharma, National Centre for Biological Sciences, India• Yoshihiko Nakamura, The University of Tokyo, Japan• Ales Ude, Jozef Stefan Institute, Slovenia• Sylvain Calinon, Italian Institute of Technology, Italy• Florention Wörgötter, Georg August University Goettingen, Germany• Giorgio Metta, Italian Institute of Technology, Italy• Tamim Asfour, Karlsruhe Institute of Technology, Germany• Patrick van der Smagt, Technical University of Munich, Germany• Nihav Dhawale, National Centre for Biological Sciences, India• Naoki Fukaya, Tokyo Metropolitan College of Industrial Technology, Japan• Minoru Asada, Osaka University, Japan• Angelo Cangelosi, University of Plymouth, United Kingdom• Yiannis Demiris, Imperial College London, United Kingdom• Angelika Peer, Technical University of Munich, Germany• Tadahiro Taniguchi, Ritsumeikan University, Japan• Tetsunari Inamura, National Institute of Informatics, Japan• Takayuki Nagai, The University of Electro-Communications, Japan• Flörian Rohrbein, Technical University of Munich, Germany

3Program
Arrival Day (Sunday, 10th November)
15:00-18:30
19:00-20:30
21:00-

Check-In Shonan Center
Welcome DinnerFree time

Day 1 (Monday, 11th November)
07:30-09:00
09:00-09:10
09:10-12:00
12:00-13:30
13:30-18:00
18:30-19:30
19:30-Breakfast
Shonan Introduction by Staﬀ
Opening briefing from organizers
Position talks from participants
Lunch
Session: Embodied Experience and Interaction I with Coﬀee breakDinnerFree TimeDay 2 (Tuesday, 12th November)
07:30-09:00
09:00-12:00
12:00-13:30
13:30-15:00
15:00-15:30
15:30-18:00
18:30-19:30
19:30-Breakfast
Session: Embodied Experience and Interaction II
Lunch
Session: Biologically Inspired Design
Coﬀee break
Session: Networked Social Robotics I
DinnerFree TimeDay 3 (Wednesday, 13th November)
07:30-09:00
09:00-12:00
12:00-13:30
13:30-18:00
19:00-21:30
21:30-Breakfast
Session: Networked Social Robotics II
Lunch
Excursion to Kamakura
Banquet DinnerFree TimeDay 4 (Thursday, 14th November)
07:30-09:00
09:00-12:00
12:00-13:30

Breakfast
Session: Wrap-up
Lunch

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Session: Embodied Experience and Interaction I
Overview of Talks
Anthropomatics and Robotics
Rüdiger Dillmann, Karlsruhe Institute of Technology, Germany”Is it from man is it from the machine or from both?” - Anthropomatics addresses this problem focusing on the symbiosis between humans and machines.
Robotics stands for automation. Modelling, representing and understanding
the sensomotoric mechanisms, learning and development of skills and cognitive
capabilities to enable humans to interact with the world is the key to design
technical systems operating closely and interactively with humans. However, theproblem of learning skills and task knowledge by observation of human activities
and the related context is still an open field. My research interest is on interactive machine learning and multimodal channels between humans and robots todesign anthropomatic systems and to discuss the underlying paradigms.

Assist-As-Needed Robotic Training for Motor Skill Learning
Tomohiro Shibata, Nara Institute of Science and Technology, Japan
How can we accelerate motor skill learning of humans? For this, obvious
obstacle is on the individual diﬀerence in their bodies and brains. Furthermore,
the guidance hypothesis states that humans tend to rely too much on external
assistive feedback, resulting in interference with the internal feedback necessary for motor skill learning. Here I present our framework for assist-as-needed
robotic training and its application to learning darts-throwing skill.

Policy learning in high-dimensional state space
Jun Morimoto, Advanced Telecommunications Research Institute International(ATR),Japan
We introduce a policy learning framework for a high-dimensional systemthrough fewer interactions with the real environment than standard RL methods. In our learning framework, we first use mental simulations to improve thecontroller parameters using an approximated environment model to generatesamples along locally optimized trajectories. We then use the approximateddynamics to improve the performance of a tool manipulation task in a path
integral RL framework, which updates a policy from the sampled trajectories ofthe state and action vectors and the cost.

Working in Alternate Spaces: Exploiting Topology and Dynamics for Eﬃcient Movement Adaptation
Sethu Vijayakumar, The University of Edinburgh, United Kingdom
Our mature work on high dimensional, online learning and adaptation, especially in the context of optimal control and apprentice learning paradigms,
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have highlighted the need for eﬃcient representations - in order for the prototype ML techniques to scale to real world problems. I will outline our recent
attempts at exploiting redundancies at the level of variable impedance as wellas using topological metrics to plan in contextual spaces, with applications tospatio-temporal optimisation of contact rich motion in dynamic environments– highlighting how working in the right spaces automatically exploits naturaldynamics of the embodied plant to deliver eﬃcient, ’natural’ solutions.

Bioinspired design of a throwing machine: Mechanics, materials and control
Madhusudhan M. Venkadesan, National Centre for Biological Sciences, India
Through measurement of humans, numerical optimization, and experiments
on a throwing machine, I will show how kinematics and elasticity form an essential part of the human ability to throw accurately at high speeds. Theseessential elastic elements in humans arise from the passive properties of ligaments, as well as the active and tunable elasticity of muscles. Using theseexamples in high speed throwing, I argue that animals are finely tuned throughevolution to create mechanical solutions for otherwise hard control problems.

Contact stability in fingers
Neelima Sharma, National Centre for Biological Sciences, India
Be it holding a mug of water, or making origami, you need to control boththe force at your fingertips and the fingers’ posture. Simultaneous control of
endpoint force and posture in a robotic finger requires accurate and fast feedback
with loop delays of less than 10ms. How then are humans, with feedback delaysof over 60ms, able to remain stable in contact? To achieve robustness to longtime delays, we develop a control scheme that is linearly stable without theneed for feedback. A proposed physical implementation of our scheme naturally
leads to cable driven mechanisms that are driven by actuators with nonlinear
elasticity like in muscles. I end with a discussion of the implications of ourresults to both robots and humans.

SummaryRüdiger Dillmann presented an overview over Anthropomatics and Robotics
and emphasized that this is a very wide field, highly multimodal, and interdisciplinary. He asked the question: How teams humans and robots can ”makeshared experience” towards the advancement of the knowledge bases of the machines. Especially there was also the question of complexity put forward. Can
one address ALL the diﬃcult problems in Anthropomatics at the SAME timein ONE system? Here asking, too, How strongly embodiment would indeed
underly higher functions.
Jun Morimoto discussed policy learning in high dimensional state spaces
and the problems of decision making using reinforcement learning methods. He
presented aspects of optimal control using a formalisms derived from the HJB
equation. Specifically he addressed the problem of assessing the value function6

(specifically in non-linear cases). The central problem seems to be the curseof too many degrees of freedom in robotics. Here he suggested as a solution
to use mental simulation (prediction) by ways of a forward model to update
the policy. Then try out the policy, then improve the model, then perform thenext prediction etc. A remaining problem is how to accurately emulate thecost/reward function in a simulation. The link to forward/inverse models in thehuman Cerebellum remains also an open question.
Tomohiro Shibata presented aspects of the smart life care community (Assistive robotics). He discussed problems of assisting learning of motor skills (modelfree) and addressed the issue of therapy, too. He emphasized the need of highfunctionality as well as individualized, ”assist-as-needed” assistance. As a casestudy he used the very complex motor control problem of dart throwing asking ”what is the essence of a motion?” versus what are random inter-individual
diﬀerences. What happens when there are patients who need non-standard controls? His answer was: It’s the goal that is trained and not the motion-as-such,
which will address this issue.
Sethu Vijayakumar addressed optimal control issues working in alternate
spaces and focused mainly on movement generation problems. An interesting
aspect was how to perform impedance transfer via EMG signals. The question
arose how much is here explicit and how much is implicit (learnable)? To thisone possible answer would be that only the TYPES of control costs need to be
specified. The actual WEIGHTS of the costs can be learned. As often it was
asked: How optimal is optimal control, how objective are objective functions?
Mainly this was discussed in view of the point that biological agents usually do
not operate under optimal control but rather clearly beneath optimality.
Madhusudhan Venkadesan presented an interesting mathematically complex
study on optimal control in biological systems using the example of throwing byhumans, which is a biomechanically diﬃcult problem but also from the viewpointof control. He focused much on the biomechanical constraints. He addressed
the diﬃcult question of the required millisecond temporal accuracy which isneeded to achieve repeatable optimal release. Here accuracy and speed areboth required for an optimal throw.
Neelima Sharma taked about the contact stability of fingers using the xxample of buckling. She described how adding stiﬀness and damping leads to improved stability and discussed the relation of muscle and joint stiﬀness.
Discussion
During the general discussion several aspects were raised:
The session ”Embodied Experience and Interaction” did not cover much the
aspects ”cognition”, ”planning” and ”social domain”
Other questions were: How can we arrive at higher levels of symbolic information? Where do they emerge? How much does prediction play a role forcognitive agents? We do NOT continuously predict stuﬀ. We rather ignore large
parts of our world and get surprised but not because of explicitly predictionfailure, rather in a reflexive way. Something that catches our attention.
The question of interactions by language was raised not only for naked communication as such but also as a means for abstraction. How to get to this inmachines?

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Scalability issues were discussed, too, especially in conjunction with the hugesensori-motor spaces with which we are faced in this field.
In Summary: The presentations in this part of the WS covered wide ranging
and diverse aspects towards the title of this topic. The discussion raised interesting and important additional aspect which may help to lead to improved
approaches in the future

Session: Embodied Experience and Interaction II
Overview of Talks
Anthropology and Robotics
Yoshihiko Nakamura, The University of Tokyo, JapanThe domain of humanoid robotics is not only to make a bipedal and bimanual robot any more. Technical challenges expand from following the human-likegeometry for robot design to asking about the use of the human geometry forthe design of robot intelligence. Scientific challenges may include investigating
the role of human geometry for human cognition. This may be better explainedby the word of ”anthropomorphism.” Anthropomorphism means how we interpret things as we see humans. To discover the principle of informatics behind
anthropomorphism is the main scientific goal of humanoid robotics. Anthropomorphism lies as the foundation to understand human psychology, humandevelopments, human behaviors, and human society. Many applications will
follow from the scope for medical care, social service, and industrial developments.

On-line adaptation of robot skills in contact with the environment
Ales Ude, Jozef Stefan Institute, Slovenia
Compliant robots can adapt their movements to account for force feedback arising in tasks that involve contact with the environment including otherrobots. We study how such movements can be adapted in a predictive way, thus
minimizing the unwanted impacts and optimizing the task execution. A robust
and compact representation of movement, which enables fast on-line modification and ensures stability, is essential for this purpose. Similar strategies canalso be applied when altering robot movements based on human in the loop
coaching gestures, where real forces are replaced with virtual force fields.

Robot learning by imitation and exploration with probabilistic dynamical systems
Sylvain Calinon, Italian Institute of Technology, Italy
Robot programming by demonstration facilitates the transfer of skills bykinesthetically guiding the robot through the task or by letting the robot observe
the user executing the task. One key challenge is that the robot needs to start8

generalizing skills from very small training sets, with a representation that needs
to be shared by other learning strategies such as stochastic optimization andself-improvement.
Designing machine learning tools with extrapolation capability seems to be a
crucial element to move the imitation challenges to more complex real-world applications. This can be achieved in various ways, such as goal-directed imitation(copying the intent underlying the demonstrations instead of directly copying
the actions), learning the underlying structure of the task, or with learning
strategies combining social interaction and self-refinement. I will show an approach jointly using statistics and dynamical systems to exploit the varying
accuracy requirements of the task.

Understanding human manipulations
Florention Wörgötter, Georg August University Goettingen, Germany
The ability to reliably manipulate objects is a major aspect that has fostered
our cognitive phylogeny and that is in the core of every human’s individual mental development. As a consequence we can understand (manipulation) actions!
Robots cannot! And the transfer of the semantics of an action onto artificial
agents has so far defied our eﬀorts. Here we will argue that actions can beunderstood as sequences of simple events, leading to a limited ontology of manipulation actions and to a direct link to the symbolic domain (language).

Exploring aﬀordances and tool use on the iCub
Giorgio Metta, Italian Institute of Technology, Italy
One of the ensuing goals in robotics is the development of various robotic
learning mechanisms that would predict the eﬀects of certain actions on objects
and improve task execution over time. It is paramount to predict the propertiesof an object from afar, for example on a table, in a rack or a shelf, which
would allow the robot to select, beforehand, an appropriate action in orderto accomplish a particular goal. Here I show an approach to the design ofcognitive skills in a robot able to interact with the surrounding physical world
and manipulate objects in an adaptive manner. I describe a general pipeline
for learning object-rolling aﬀordances, tool exploration and tool handling. I
will show successful learning in the real world where the iCub humanoid robot
interacts with objects.

It’s All about Force
Tamim Asfour, Karlsruhe Institute of Technology, Germany
Considerable progress has been made towards the realization of humanoid
robot systems which are able to move in a human-like way and perform tasks
in human-centered environment. However, versatile 24/7 humanoid robot systems integrating perception, action, prediction, planning and lifelong learning
capabilities in the real world are still missing.

9In this talk I will discuss the role of force in the formalization and unification of sensorimotor and semantic representations and introduce the force
space as unifying space of perception and action and mechanism for bootstrapping the development of high performance 24/7 humanoid robots. I will argue
that objects, agents and their actions can be described based on a new concept of sensorimotor force fields (SFF) which provides a unified representationand computational mechanism for solving tasks related to grasping, dexterous
manipulation, balancing and robot control. SSFs are based on the assumptionthat diﬀerent (all) types of sensory modalities such as position, pressure, tactile,
audio and vision do share significant commonalities with each other as they canbe mapped into the same space: The force space. This does not only provide
a unifying representation of perception and action in the same space but also
will bootstrap the development of new generation of high performance robotics
systems.

Session: Biologically Inspired Design
Overview of Talks
Building useful robotic hands
Patrick van der Smagt, Technical University of Munich, Germany
Studying the human hand, one cannot help noticing that a number of its
kinematic and static properties are important for grip stabilization and manipulation. Also, the human hand’s neural control structure, diﬀering considerablyfrom arms and legs, demonstrate its special role in the human’s body. I will
highlight a few of the hand’s properties, from an engineering perspective, anddemonstrate how these properties can be exploited in robotic hands.

Stability and energetics of running on uneven terrains
Nihav Dhawale, National Centre for Biological Sciences, India
Stable running requires that the kinetic energy of the runner is mainly associated with a nearly periodic mode of movement. Uneven terrains challenge
stability by redirecting the kinetic energy of the runner into other, orthogonal modes of movement, falling can be a consequence of this. Any strategy toremain stable should absorb energy associated with the undesired modes, andinject it back into the desired mode. We propose a minimal model for running locomotion, and show how energy and stability trade-oﬀ when running on
uneven terrains. I discuss how such a model can be used to understand thefeedback and open-loop strategies employed by human runners.

Development of good hardware made by unique, smart andnoble mechanical design
Naoki Fukaya, Tokyo Metropolitan College of Industrial Technology, JapanThe good hardware made by unique, smart and noble mechanical design
realizes small size and a weight saving, and makes structure simple. It leads to10

improvement in reliability and durability. Moreover, it realizes the reductionof incidence of a control system. Our target is to imagine and develop themechanical design with many merits.

Session: Networked Social Robotics I
Overview of Talks
How to design artificial emotion
Minoru Asada, Osaka University, Japan
Emotion, a driving force to generate diﬀerent behaviors, is one of the mostfundamental but diﬃcult structures/functions to design for robots. Startingfrom primitive emotions, the secondary emotions may be diﬀerentiated fromthem. During this developmental process, sociality has an important role toderive the diﬀerentiated emotions. In this talk, I argue how artificial emotion
can be more realistic in the social context by showing some attempts, and discuss
the future stories in SFs and comics.

Embodied Language Learning in Robots
Angelo Cangelosi, University of Plymouth, United Kingdom
The talk will introduce the issues of symbol grounding and language embodiment in cognitive and neural sciences and show how this has contributed
to developmental robotics experiments on language and action learning andinteraction.

Can you learn this? Recognising and assisting children’s
actions for optimising their long-term sensorimotor development
Yiannis Demiris, Imperial College London, United Kingdom
As educators, mentors, and parents, we do not assist our less experienced
ones constantly and unconditionally. We do so on the basis of individual models
we have built based on our previous interactions. We give them tasks that areneither outside their reach, nor trivial for their skillset, but challenge them optimally. Determining the learnability of a new task is a challenging and complex
inference process that combines models of the development of an individual,with statistical models of typical developmental trajectories at the populationlevel. In this talk I will describe my research in a principled computationalframework for modelling and assisting sensorimotor development, and describe
its application for assisting children: example tasks include robotic wheelchairnavigation for children with disabilities, dancing, and learning car racing games.

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Embodying yourself into (remote) robot avatars
Angelika Peer, Technical University of Munich, Germany
Having the possibility to embody oneself into a robot avatar allows to perform manipulation tasks at a distance, to enter human inaccessible environments
or to overcome limitations of the own sensorimotor system, e.g. in case of paralyzed people. Control architectures for such systems range from bilateral, sharedto supervisory control depending on the specific capabilities and cognitive stateof the human operator. I will discuss typical challenges in realizing such systems
ranging from robust stability, transparency, intention recognition, and authority sharing to the personalization of robot actions and show examples of robot
avatars controlled by means of force/motion signals as well as brain and bodycomputer interfaces.

Summary
In this session, we discussed about networked social intelligence including emotion, language and shared autonomy. First presenter, Minoru Asada, gave atalk about “How to design artificial emotion”. Emotion, a driving force togenerate diﬀerent behaviors, is one of the most fundamental but diﬃcult structures/functions to design for robots. After introducing his projects and approaches to developmental robotics, he introduced several future stories in SFs
and comics for discussing artificial emotion. Secondary, Angelo Cangelosi told
about “Embodied Language Learning in Robots”. Starting from symbol grounding problem, he explained the importance of embodiment for language acquisition problem. We discussed about learning system for language acquisition,
abstraction and embodied cognition. The third speaker was Demiris Yiannis.
The title of his presentation was “Can you learn this? Recognizing and assistingchildren s actions for optimizing their long-term sensorimotor development”.
He introduced his research topics including assistive wheel chair system androbotic system which help children learn dancing, and so on. Finally, Angelika
Peer told about “Embodying yourself into (remote) robot avatars”. She introduced telexistence technology she has been studying. Control architectures forsuch systems range from bilateral, shared to supervisory control depending on
the specific capabilities and cognitive state of the human operator. We discussed
related topics to the telexistence.

Session: Networked Social Robotics II
Overview of Talks
Symbol emergence in robotics
Tadahiro Taniguchi, Ritsumeikan University, Japan
Humans can acquire language through physical interaction with their environment and semiotic interaction with other people. To understand computationally how humans can obtain semiotic skills through their mental development is very important problem. In the research field of symbol emergence12

in robotics, we are challenging to construct a robotic system which can obtain
language through embodied multimodal interaction. I also introduce double articulation analyzer (DAA) which is a nonparametric Bayesian machine learningtechnique. DAA can extract latent double articulation structure in a semiotic
time-series data, e.g., human motion and driving behavior, in unsupervised way.

Simulator Platform for Cognitive Social Robotics
Tetsunari Inamura, National Institute of Informatics, Japan
Research on high level human-robot interaction systems that aim skill acquisition, concept learning, modification of dialogue strategy, and so on requires
large-scaled experience database based on social and embodied interaction experiments. However, if we use real robot systems, costs for development ofrobots and performing various experiments will be too huge. If we choose virtual robot simulator, limitation arises on embodied interaction between virtual
robots and real users. Our group thus proposes an enhanced robot simulator that enables multiuser to connect to central simulation world, and enables
users to join the virtual world through immersive user interface. As an example
task, we propose an application to RoboCup@Home tasks. In this talk, I explain the configuration of our simulator platform and feasibility of the system
in RoboCup@Home.

Long-term Learning of Concept and Word by Robots
Takayuki Nagai, The University of Electro-Communications, JapanOne of the biggest challenges in intelligent robotics is to build robots that canunderstand and use language. Such robots will be a part of our everyday life; atthe same time, they can be of great help to investigate the complex mechanismof language acquisition by infants in constructive approach. To this end, I
think that the practical long-term on-line concept/word learning algorithm for
robots and the interactive learning framework are the key issues to be addressed.
This talk highlights our eﬀorts on the development of such a practical on-line
learning framework based on Bayesian learning, and some preliminary results
on experiments using a real robot platform to show its potential toward theultimate goal.

Neurorobotics in the Human Brain Project (HBP)
Flörian Rohrbein, Technical University of Munich, Germany
The HBP will develop six ICT platforms, dedicated respectively to Neuroinformatics, Brain Simulation, High Performance Computing, Medical Informatics, Neuromorphic Computing and Neurorobotics. The Neurorobotics Platform
will allow researchers to conduct closed-loop experiments, in which a virtual
robot is connected to a brain model, running on the HPC platform or on neuromorphic hardware. This platform will be the first robotics environment thatallows to couple robots and detailed models of the brain and to explore the links
between neural activity and high level brain functions. It will be designed for13

non-robotics and robotics researchers alike and will go far beyond the capabilities of current platforms for experimental robotics by allowing researchers frommany disciplines to design and perform robotics experiments.

Summary
The Wednesday AM session on networked social robotics focused on two issues:(i) multimodal categorization and language (and time-series) learning for HRI(Nagai and Taniguchi), and (ii) the contribution of robotics simulators to hybrid
virtual/physical HRI (Inanura) and to neurorobotics (Röhrbein).
Nagai presented a multimodal categorization and language learning system
for human-robot communication. The key concept on his approach was multimodality, in particular through the implementation of a Bag of Feature model for
multimodal signal processing. This was based on three input modalities on theobject being categorized/named (visual DSIFT, non-speech auditory MFCC,
and tactile information) and the additional speech modality of the teacher’sword utterance. This method is based on unsupervised classification and statistical, graphical model MLDA (Multimodal Latent Dirichlet Allocation). A
key component of the model is also the online interaction and learning withthe user. One experiment on 20 objects x 5 categories Showed the correlation
between Mean-Length Utterances and categorization. Another novel experiment on long-term interaction and learning sessions involved 200 objects, witha preliminary experiment based on 1000 utterances over a week. Moreover, theMultilayer Multimodal LDA is being proposed and tested for categorization of
motion, e.g. for the meaning of the action/verb ”shake”.
Taniguchi focused on symbol emergence in robots within the context of developmental psychology of semiotic communication. He support an embodiment,
constructivist approach for evolving symbol systems, with concepts learnedbottom-up from sensorimotor information and via social interaction and constraints. To operationalize such an approach he uses The Double Articulation
Analyzer (DAA) method as an unsupervised, non parametric Bayesian learning system. In general, this can be considered as a method for the segmentation of sensorimotor time-series information, which ranged from pure linguistic
tasks (segment speech time series into words) to other series as human motion
data, driving behavior. Chunking of driving; chunk separation corresponds tochanges og the driver’s intention For example, in his ongoing work on unsupervised morphological analysis to segment sentences into words (based on NPLYM,
Nested language model) he also proposes a balance of supervised and unsupervised learning: hybrid model using supervised labels as part of the unsupervised
learning.
The other two talks have a key focus on robot simulators. Inamura proposes
the use of simulators to address the issue of high cost of HRI with physicalrobots (e.g. robot building/maintenance + experiments/participants time andmoney costs). Hybrid systems based on virtual/physical robots and agents also
oﬀers the extra benefits of long-term HRI experiments with participants entering the virtual simulator word and interact with the virtual robot to teachit new words and concepts. Examples of his previous work on HRI on virtualand physical robots include learning by demonstration for humans (sports coachrobot), geometric representation of sensorimotor patterns, and interpolation andextrapolation of movements (squat/punch experiments). The SIGVerse robot
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simulator was briefly presented, specifically for its role in embodied social intelligence experiments and the bridging between Virtual and Real worlds (though
WWW-based login interaction, VR Headset and Kinect). He also presented
the use of the SIGVerse software in the RoboCup@Home project for daily environment task simulator (clean-up task in JapanOpen2013). Other application
areas include learning of manipulation skills, driving simulator for multi-users,
language acquisition experiments. This tool also oﬀers research application opportunities in a variety of contexts and disciplines, and the dream to reaching
the wider beyond-robotics community: macrosimulations, crosscultural studies,
language evolution models, developmental modeling, animal modeling.
Röhrbein introduced the HBP project with its main double aim to (i) understanding by building; (ii) developing more intelligent IT systems and robots
and the three research areas of Future neuroscience, Future medicine, Futurecomputing. In particular, details on the NeuroRobotics SubProject were presented. The first 2.5 year ramp-up phase is specifically aimed at the design ofa general neurorobotic simulator for both roboticists and neuroscientists. The
aims of this simulator are (i) to develop principles and build (larger) community;(ii) simulate all constituents (robot body, behavior, environment) with highest
fidelity robot simulator; (iii) the grounding of these simulations in real world
systems and closed-loop robotics experiments.
The key general issues and open challenges for future research resulting fromthis session are:• Multimodal categorization and language learning approaches• Close integration of both sensorimotor and social mechanisms in HRI andlanguage learning• Developmental and constructivist approaches to language learning in robots• Importance of long-term HRI studies (training the robot for weeks andmore)• Contribution of hybrid virtual/physical robot experiment/simulation tools
to extended the reach beyond the restrict robotics community• The use of simulator and closed-loop systems to validate neurorobotics
theories and hypotheses• The multi-scale level of neurorobotics studies (from molecular, compartment models, to spiking networks, to average firing rate models)• Importance of closed-loop approaches in neurorobotics

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