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Laboratory of Computational Embodied Neuroscience

 

LOCEN is a research group of ISTC-CNR.
LOCEN ``mission'' is to use computational and robotic models to investigate how brain acquires behaviour, in a cumulative fashion, by interacting with the body and the environment; and to exploit the knowledge so acquired to build autonomous cumulative-learning humanoid robots. 
LOCEN was founded in 2006 and is a very dynamic and interdisciplinary research group formed by 8 young researchers plus 4 undergraduate students.
LOCEN research is funded by European Projects (with the exception of Gianluca's and Marco's salaries). 
LOCEN uses the research methods of "Developmental Robotics" and "Computational Embodied Neuroscience". 

LOCEN research topics are: robotic autonomous learning and development, extrinsic and intrinsic motivations, goal-based learning, hierarchical reinforcement learning, attention and active vision, motor control and muscles, habitual and goal-directed behaviour and learning, classical and instrumental conditioning, brain mechanisms underlying these processes in animals (e.g., amygdala, hippocampus, basal ganglia, frontal cortex, dopamine). 

Address: 

gianluca.baldassarre@istc.cnr.it

Physical address and how to reach us: click on CONTACT tab

LOCEN Intranet (limited access)

Profile

LOCEN: introduction

LOCEN investigates cumulative open-ended learning (its ``mission'') using two methodological approaches: (a) developmental robotics, focussed on building useful robots; (b) computational embodied neuroscience, focussed on understanding brain and behaviour of animals. The group uses these approaches to investigate two distinct but related sets of topics and problems. The two methods and the two sets of topics are now illustrated in detail.

LOCEN autonomous robotics: method

The research agenda and bio-inspired approach of the group

The group has a research agenda that aims to build autonomous cumulative learning robots. This are robots that are capable of autonomously acquiring a number of skills in autonomous fashion, without human intervention. LOCEN_iCubBoardAlthough the group fully recognise the great importance of social mechanisms for the acquisition of complex behaviours like those acquired by primates (e.g., social enhancement, joint attention, imitation, language, teaching, etc.), the group focusses its research on individual learning processes so to have a higher impact in this study. In this respect, the ``holy grail'' of LOCEN is to arrive to build robots that, like children, are capable of acquiring an increasingly sophisticated repertoire of sensorimotor and cognitive skills, from simple to complex ones, in a cumulative fashion, without external intervention. For this reason, we follow the developmental robotics approach to build its robots.

In this respect, the group believes that fundamental breakthroughs in specific fields of robotics and machine learning will come from the study of biological systems. The reason is that organisms are the result of a run of the ``gigantic'' genetic algorithm represented by natural evolution: such algorithm has found solutions to some problems that engineering approaches will not beat for long. The idea is so to reverse-engineer brain and behaviour of real animals to propose radically new ideas to autonomous robotics and machine learning, that might synergies with those discovered with more traditionalapproaches. 

LOCEN autonomous robotics: topics

The topics investigated by the group are all directed to support its ``mission'' of building autonomous, cumulative learning robots. We believe that four critical challenges need to be overcome to accomplish this goal: 

Dynamic behaviour, compliant hardware and muscles 

All that ultimately counts, for both robots and animals, is action. Action means a change of the sensorimotor body in space that in turn can change the environment (

e.g., to manipulate objects) and the body-environment relation (e.g., to look at something, or to change position in space). Behaviour emerges from the dynamic interplay between the robot's action, that changes the body-environment relation, and the close-loop physical and perceptive feedback flow that results from this. Part of the extraordinary capabilities that animals display in these interactions rely upon the hardware of their body, in particular  their compliant skeleto-muscular system. For this reason, one topic of interest of the group is the challengs and opportunities offered by robots endowed with a biomorphic compliant hardware (e.g., bio-materials, biologically-plausible structure, pneumatic actuators).  

Active vision, proprioception and abstraction 

The sensorimotor flow brings to robots and organisms a huge amount of information. The group focuses on proprioception, touch, and vision. Proprioception is used to perform dynamic motor behaviour. LOCEN_EyesHandsVision is instead used to guide behaviour at a higher level, e.g. to visually guide reaching and grasping, or to decide with which object to interact. We see perception not as a passive source of information, but rather as an active process of search of relevant information and avoidance of non-useful information given the goal pursued by the robot/organism. At both the perceptive and motor level, behaviour needs abstractions. For example, when an object is seen it is useful to abstract a number of distinct information elements used by different downstream processes of the controller, e.g. its location, size, identity, etc. Active vision and abstraction processes are so an important investigation topic of the group. ouch are used as a fundamental element to implement sensorimotor skills at the low level.
  

Extrinsic and intrinsic motivations, and learning 

As the group is interested in autonomous cumulative learning, he is profoundly interested on the mechanisms that can guide learning in an open-ended fashion without external intervention. Motivations are a key element for this as they can: (a) drive the robots to perform different behaviours in different conditions; (b) generate the learning signals needed to guide their learning. Two types of motivations are studied by the group. The first are extrinsic motivations, i.e. motivations related to the task assigned to the robot (what is the best reward function function?). The second are intrinsic motivations. These are a rather novel concept in autonomous robotics and the group is keenly investigating them. Intrinsic motivations drive behaviour and generate learning signals on the basis of the fact that the controller is indeed acquiring knowledge with a high rate, e.g. is forming good abstractions, is learning to predict well, or is acquiring the sensorimotor competence to accomplish a goal. Intrinsic motivations are fundamental for autonomous robots as allows them to acquired knowledge and skills in the absence of tasks from the user, so that when some tasks are assigned to them they can exploite the knowledge acquired autonomously to readily solve them. The pivot of all these processes is of course learning, so the group keenly investigates all forms of learning useful for robots, from associative to unsupervised learning, from supervised to reinforcement learning (the latter is central in several models of the group). 

A video showing a humanoid robot (iCub) that explores a `mechatronic board' under the drive of intrinsic motivations, and autonomously discovers and learns that pressing some buttons turns on some lights. In a later stage, the robot exploits the acquired action-outcome contingencies to accomplish useful purposes. For more detailed videos see here and here: these videos are explained in the web-pages here and here; the system is explained in detail in the article here. Work carried out within the project IM-CLeVeR.

Hierarchical architectures 

The cumulative acquisition of skills and cognitive capabilities requires that the newly acquired knowledge: (a) is stored without destroying previously acquired knowledge; (b) supports the acquisition of further knowledge. The first goal is achieved through hierarchical architectures that avoid that the acquired different pieces of knowledge interfere with each other. The second goal is accomplished by investigating how the acquired knowledge can be transferred to new tasks to be solved (e.g., as in transfer reinforcement learning). Because of these two goals, the group is keenly interested in developing hierarchical reinforcement learning architectures. 

Video of a humanoid robot (iCub) learning to throw a ball to a target based on a hierarchical reinforcement learning system with sophisticated generalisation capabilities (generation of new dynamic movement primitives on the fly based on the similarity of the new goal with respect to previously acquired goals). This work was carried out in collaboration with Bruno Castro da Silva and Andrew Barto, from the University of Amherst Massachusetts.

LOCEN brain and behaviour: method

Computational Embodied Neuroscience 

The group has developed his own research method to study brain and behaviour, named Computational Embodied Neurocience (CEN). Differently from other computational approaches aiming to understand computational neuroscience, CEN aims to understand the brain with a ``top-down approach'', i.e., roughly speaking, it aims to understand the brain starting ``from behaviour rather than from ion-channels''. The key idea behind this is that the brain evolved to allow animals to act so as to improve their survival and reproductive chances. So to fully understand how brain works, we need to understand not only its mechanisms (anatomy and physiology, the common focus of neuroscience) but also its functions. This is why the group is keenly interested in linking the knowledge produced not only by neuroscience but also by psychology and psico-biology.

From system-level to detailed models 

Another consequence of the goal of CEN of studying whole systems capable of acting is the tendency to build system-level models, reproducing the macro-architecture of various cortical and sub-cortical brain areas that underlie the target behaviours. This is in fact needed if one wants to understand how a certain area of brain works not studying it in isolation, but how its inner mechanisms play a certain function within a whole system. Of course, often the aspect of the system which are not under focus are represented in an abstract fashion, but they are nevertheless there. After sedimenting knowledge at the system-level, the group usually starts to refine the micro-architecture and functioning of the various components of the model (usually focussing on a subset of them). For these overall purposes, our models are usually based on firing rate neurons and leaky neurons, but not on spiking neurons. Recently, the group has started to build probabilistic graphical models whose functioning is implmemented on the basis of Bayesian inferences. These models have the advantage of facilitating system-level modelling at a high (often functional) level before moving to more detailed neural models, and also of offering the possibility to directly evaluate the goodness of models against empirical data, for example from brain imaging.

Aiming to build cumulative models 

The hallmark of science is cumulativity. Too often different computational models are build to  interpret different experiments. Instead, CEN aims to build models that allow the explanation of an increasing number of specific brain and behavioural data. This allows the isolation of general principles, the integrated theoretical systematisations of whole classes of phenomena, and so to help psychology and neuroscience to overcome the polverisation of results and views that they often encounter for their need of focussing. Integrated theories and models allow the production of detailed hypotheses that fill in the knowledge gaps of psychology and neuroscience and produce specific empirical predictions that can be tested in new empirical experiments. 

The importance of a close dialogue between models and empirical data 

Differently from other computational approaches to the study of brain and behaviour, CEN stresses the importance of having a tight, continuous dialogue with specific empirical data from psychology and neuroscience. The idea is that the understanding of brain and behaviour should proceed as any good science, namely it should rest on two pillars: (a) the theoretical understanding of the investigated phenomena, based on formal computational models; (b) the empirical investigation of such phenomena to select the best theories, models, and predictions.  In this respect, we see computational modelling as a powerful theoretical means that should guide empirical research on brain and behaviour. The ultimate proof that computational modelling of brain and behaviour has successfully accomplished its mission is its capacity to change the daily research of the empirical neuroscientist and psychologist, and to publish papers in top journals of neuroscience and psychology. 

The importance of embodiment: sensorimotor and visceral 

We believe that brain generates behaviour by dynamically interacting with the environment through sensors and actuators in a circular fashion (embodiment). Sensors furnish a rich, redundant and noisy amount of information to organisms. Actuators are in turn noisy, redundant, compliant. Before facing high-level cognition problems, brain has to solve the problems posed by such input/output information channels (and also exploit the opportunities they offer) . The resulting computations might be radically different from those that would stem from, say, a clean, symbolic type of input/output information. For these reasons, we think that good models of brain and behaviour should function in simulated or real robotic systems that have the same sensors and actuators as the investigated animals. This poses strong challenges to models, especially because neuroscientists and psychologists often overlook them and require us to focus ``on their problems'', concerning higher-level aspects of cognition. For this latter reason, the more biologically constrained models produced by the group so far often use localistic representations, abstract input/output information codes, simplified environments. However, the group is fully aware of the importance of scaling up models  to more realistic input/output and environmental conditions, so we try our best to incorporate in the models critical elements of a true embodiement, e.g. the sensorimotor loop and the test with simulated/real robots.  Aside this, the group gives also a lot of important to a second type of ``embodiment'', most of the times neglected but as important as ``sensorimotor embodiment''. This might be called ``visceral embodiment'' and refers to the key relation that the brain has with the visceral body and its homeostatic regulations. These regulations are at the basis of extrinsic motivations and the subjective value (biologically saliency) that organisms assign to objects and experiences. 

The importance of learning

We believe that for a large part the brain structure is as it is because it has not only to express behaviour but also to learn it. For this reason, most of our models aim not only to reproduce target behaviours, and the neural machinery to do so, but also the learning processes that lead to its acquisition with experience, and hence the physiological processes underlying this. For this reason we are keenly interested in studying all forms of biologically plausible learning: Hebbian learning, differential Hebbian learning,  competitive self-organised learning, and reinforcement learning, and goal-based learning.

LOCEN brain and behaviour: topics

Given the interest of the group in understanding how brain and behaviour supports cumulative learning in organisms, our research is focussed on the following topics:

Vision and active vision (superior colliculus, brain dorsal and ventral pathways, basal ganglia, cortex)

The bio-contrained models developed by the group focus on two types of perception: proprioception and vision. Often we use proprioception in our models to guide low-level behaviour, but we do not study it per se.  We instead are very interesting in studying vision, and especially active vision. The reason is that vision is the primary information source for primates and has a paramount role in guiding action (via the brain dorsal pathway, involving parietal and premotor cortex) and to support higher-level cognition, such as decision making and planning (via the ventral pathway, involving inferotemporal cortex and prefrontal cortex). We study vision not as a passive source of information but rather as an active one. In particular, we are interested in studying how overt attention, and the high resolution of the fovea with respect to peripheral vision, can actively search and gain information in the environment, and ignore irrelevant one, on the basis of the animal's goals. We are thus interested in bottom-up attentional processes, that drive eye gaze on most informative parts of the environment, and in top-down attentional processes, that drive the eye to collect information based on goals;  and we are of course interested in their rich interplay. We also believe that attention is pivotal for the rest of behaviour and indeed there is a strong coupling between vision and arm/hand manipulation actions, with attention representing a powerful guidance for controlling such actions via the selection of suitable inputs (``eyes lead, arms execute'').

Extrinsic motivations, Pavlovian and instrumental learning (amygdala, nucleus accumbens, dopamine)

Behaviour needs to be driven. Learning needs to be guided. Extrinsic motivations are expressed by parts of brain (amygdala, nucleus accumbens, dopaminergic and other neuromoduatory systems) at the interface between visceral body and cognitive processes. LOCEN_DevaluationWe are interested in studying how extrinsic motivations drive behaviour, directing it to specific activities, or how they generate learning signals that guide learning processes. In this respect, we are very interested in investigating how areas such as the amygdala can perform Pavlovian associations that allow triggering important internal reactions (e.g., for the internal regulation of visceral body and the neuromodulation of brain)  and external behavioural reactions (feeding, approaching, orienting) in correspondence to biologically salient stimuli (e.g., food, water, sex) or stimuli anticipating them (conditioned stimuli). Also, we are very interested in understanding instrumental behaviour, i.e. the processes that allow organisms to learn to trigger (learned, instrumental) behaviours, when particular conditions are present, if this leads to rewards (S-R behaviour).

Intrinsic motivations (superior colliculus, hippocampus, dopamine, noradrenaline)

Aside extrinsic motivations, we are interested in studying intrinsic motivations, i.e. the motivations at the core of the performance and acquisition of actions ``for their own sake'', i.e. not for the achievement of results that directly increases biological fitness (e.g., food or money). Intrinsic motivation systems have evolved as they drive exploration and learning in the absence of extrinsic outcomes, and have the function of leading animals to acquire knowledge and skills that will be readily usable in the future when such extrinsic outcomes become available.

LOCEN_CLEVERB2For example, intrinsic motivations are maximally apparent in children at play: in the absence of homoeostatic drives, children engage in ludic behaviours driven by curiosity, novelty, surprise, and changes in the environment, in general all experiences that cause an improvement of their knowledge and skills. Neuroscience is unrevealing some of the brain mechanisms behind these processes, e.g. the capacity of hippocampus to cause dopamine production (learning signals) when a novel object is perceived, or the capacity of superior colliculus to produce phasic dopamine when the world change, or the capacity of frontal cortex to cause noradrenaline production when predictions are violated.

Hierachical sensorimotor brain, habitual behaviour (hierarchical cortex, multiple basal ganglia loops,  cerebellum)

LOCEN_BrainPathwaysOrganisms' cumulative learning of sensorimotor behaviour and higher-level cognition requires a hierarchical soft-modular brain architecture that links motor behaviour to perception at different levels of abstractions and coupling. This architecture is organised in at least three levels, investigated by the group with system-level models. (1) At the lowest level, the close-loop between somatosensory cortex and primary motor cortex (forming a loop with sensorimotor basal ganglia involving putamen/globus pallidum) and involving cerebelllum, implements the dynamic production of movements (e.g., a reach, a grasp); (2) the dorsal brain pathway, encompassing visual cortex, parietal cortex (encoding affordances), and premotor cortex (encoding repertoires of actions) (which forms a loop with sensorimotor basal ganglia involving putamen-caudatum/pallidum-subtantia nigra reticulata), implement the on-line control of action (e.g., to guide a reach to an object, or to shape the hand to grasp it); (3) the ventral brain pathway, encompassing the visual cortex, the inferotemporal cortex (for object recognition), and the prefrontal cortex (for working memory and multimodal sensory integration) (which forms a loop with associative basal ganglia involving caudatum/pallidum), implements high-level decision making and executive control of behaviour; (4) the cortex communicating with sub-cortical limbic structures, such as amygdala and hippocampus (which forms a loop with limbic basal ganglia involving nucleus accumbens), processes value and encodes biologically salient action-outcomes and goals (e.g., food). The group studies this complex hierarchical system by usually focussing on different parts of it but always keeping in mind the overall architecture.

Goals, goal-directed behaviour, decision making (amygdala, hippocampus, nucleus accumbens, prefrontal cortex)

Starting form sensorimotor behaviour, the group is now gaining knowledge and skills for the investigation of higher-level cognition, in particular in relation to goal-directed behaviour and decision making (levels 3 and 4 of the framework of the previous point). ``Goals'' are now becoming a critical concept for the group for the pivotal role they play in cognition and, in particular, in autonomous cumulative learning. A goal is an internal representation of a future world state that is activated internally and drives action (and learning) for its accomplishment. The current investigation of ``goal-directed behaviour'' and decision making, strongly supported by theory-driven model-free and models-based reinforcement learning, is one of the hottest fields of investigation of cognitive science. Goals also play a key role in autonomous cumulative learning: a critical function that intrinsic motivations play is the self-generation of goals. If you look with attention a little child at play, you will realise how her/his autonomous exploration and learning is strongly guided by an incessant autonomous setting, pursuit, accomplishment, monitoring, and switch of goals. Goal-based processes, greatly enhanced by the uniquely-developed human prefrontal cortex, focus attention, inform on success, drive learning, and organise action in ways that render autonomous learning ``explosive'', i.e. powerful, omni-directional and open-ended.

Concluding remarks

These broad interests of the group can be carried on as the different members of the group are linked by the group mission but at the same time have developed an expertise in the different methods and topics listed above. Moreover, these broad interests and approaches give the group a number of advantages:An approach to autonomous robotics and cognitive-science problems that is profoundly interdisciplinaryThe capacity to coordinate, and play a key role, in large robotic and cognitive-science projects involving different approaches, themes, and teamsLast, and most important, an ``eagle-eye view'' on robotics and cognitive-science issues that allows the group to see phenomena, problems, and solutions overlooked by other more focussed, but possible near-sighted, views and approaches.

Research

Videos from the project IM-CLeVeR

 

This page presents the video of the model ''CLEVER-B'' developped within the project ''IM-CLeVeR -- Intrinsically Motivated Cumulative Learning Versatile Robots'' coordinated by LOCEN.

IM-CLeVeR studied how intrinsic motivations and hierarchical sensorimotor architectures allows organisms and robots to autonomously learn repertoires of sensorimotor behaviours by interacting with the environment.

The next video shows the robot iCub learning to interact with  a mechathronic board based on intrinsic motivations. The video describes what happens and how the robot learns.

 

 

The next video shows a situation where a coloured card-board is put in front of a box. The card-board represents a reward that is given to the robot if the robot opens the box:

 

 

The next video shows a situation where two coloured card-boards are put in front of two boxes: the card-boards represent rewards that are given to the robot if the robot opens the related boxes. At a certain point, one reward is diminished of value (is ``devalued''): the video shows how the robot does not try to open the corresponding box anymore.

 

 

The next video shows a situation that integrates the previous two tests:

 

 

People

Coordinators

Gianluca Baldassarre
Researcher
Marco Mirolli
Researcher

Administrative Staff

Researchers

Post-Docs

Post-Doc
Post-Doc

PhD Students

PhD Student
PhD Student

Associate Researchers

Associate Researcher
Former members 
  • Stefano Zappacosta (Post-Doc) (April 2006 - June 2009)
  • Fabian Chersi (Post-Doc)  (June 2009 - April 2011)
  • Dimitri Ognibene (Post-Doc) (March 2006 - April 2009)
  • Alberto Venditti (Research Fellow) (September 2006 - March 2007)
  • Massimiliano Schembri (Research Fellow) (April 2006 - December 2009)
  • Tomassino Ferrauto (PhD student)  (January 2006 - February 2009)
  • Angelo Rega (Research Fellow) (March 2006 - June 2006)
  • Paolo Tommasino (Master student, Research Fellow) (June 2011- July 2012)
  • Vincenzo Fiore (PhD student)  (October 2008 - October 2012)

Publications


Journal articles

   2014
Fiore V., Mannella F., Mirolli M., Latagliata E., Valzania A., Cabib S., Dolan R., Puglisi-Allegra S., Baldassarre G. Corticolimbic catecholamines in stress: a computational model of the appraisal of controllability. In: Brain Structure and Function, pp. e1 - 15. Springer Berlin Heidelberg, [Online First 04 February 2014]  image   image   image
Fiore V., Sperati V., Mannella F., Mirolli M., Gurney K., Firston K., Dolan R., Baldassarre G. Keep focussing: striatal dopamine multiple functions resolved in a single mechanism tested in a simulated humanoid robot. In: Frontiers in Psychology, vol. 5 (124) pp. e1 - 17. Frontiers Media S.A, 2014.  image   image

Contribution to Book/Monograph

   2014
Santucci V. G., Baldassarre G., Mirolli M. Cumulative learning through intrinsic reinforcements. In: Evolution, Complexity and Artificial Life. pp. 107 - 122. S. Cagnoni, M.Mirolli, M. Villani (eds.). Berlin: Springer, 2014.  image   image   image

Abstracts

   2014
Cartoni E., Moretta T., Baldassarre G. Specific Pavlovian-instrumental transfer: relationship with instrumental reward probabilities. In: SBDM 2014 - Fourth Symposium on Biology of Decision Making, 2014 (Paris, France, 26-28 May 2014).   image
Castro Da Silva B., Baldassarre G., Konidaris G., Barto A. Learning parameterized motor skills on a humanoid robot. In: ICRA2014 - 2014 IEEE International Conference on Robotics and Automation (ICRA) (Hong Kong, China, 31 May - 5 June 2014).   image
Mannella F., Baldassarre G. Learning and selecting actions: a computational model of the basal-ganglia cortical dynamic interplay. In: SBDM 2014 - Fourth Symposium on Biology of Decision Making, 2014 (Paris, France, 26-28 May 2014).   image

Projects

Thu, 01/01/2009 - Tue, 30/04/2013
Sun, 01/01/2006 - Thu, 31/12/2009
Fri, 01/10/2004 - Mon, 31/12/2007

Contact

Gianluca Baldassarre, Ph.D.,
Laboratory of Computational Embodied Neuroscience,
Istituto di Scienze e Tecnologie della Cognizione,
Consiglio Nazionale delle Ricerche (LOCEN-ISTC-CNR),
Via San Martino della Battaglia 44, I-00185 Roma, Italy
E-mail: gianluca.baldassarre@istc.cnr.it
Tel: +39 06 44 595 231
Fax: +39 06 44 595 243

How to get to the ISTC-CNR: Maps and directions

DIRECTIONS

From the airport “Roma Fiumicino”

This is the main airport of Rome (see map below). Follow the indications for the train terminal inside the airport. At the train terminal, buy the tickets at the ticket office for the train “Leonardo Express” to “Roma Termini” central station, where you have to get off (last stop). The ticket can be purchased at the ticket office, at the news agents and at the automatic ticket machines (cost: 11 euros). The train runs from 6.30 am to 11.30 pm, and departs every 30 minutes. Once at Termini Station, you can reach ISTC-CNR on foot (10 minutes, see map below) or take the subway. To take the subway, look for signs of subway B, Rebibbia direction (“Metro B, direzione Rebibbia”; Rome has only two subway lines, A and B.). Buy tickets from the automatic ticket machines (cost: 1 euro). Get off after one stop at subway stop “Castro Pretorio”. ISTC-CNR, a brown historical two-floor building, is round the corner of this subway stop at the beginning of Via S. Martino della Battaglia, at the entrance number 44 (see map below).
If you want to spend less money for the train, at Fiumicino airport take the train to “Roma Tiburtina”, where you get off (the trip takes about 45 minutes as this is a local train; this is usually not the last stop). At the train terminal, you can buy the ticket for this train (cost: 4.5 euros). Once at Tiburtina Station (the second biggest station in Rome), take the subway B, Laurentina direction (“Metro B, direzione Laurentina”). Get off at subway stop “Castro Pretorio”. As explained above, ISTC-CNR is round the corner of this subway stop.
The taxi from Fiumicino airport to Rome costs about 40 euros (supplements might be asked for luggage, night-time runs and public holidays) and the trip takes approximately 45 minutes.

From the airport “Roma Ciampino”

This is the second airport of Rome, and is very small. It is closer to Rome than Fiumicino airport (see maps below). Get out of the airport terminal and ask the bus drivers standing outside, near the buses you see once out, about a bus that takes you to Anagnina Subway Station (“Stazione della Metropolitana Anagnina”). The subway of Anagnina Station is on the subway line A. Once at Anagnina Station, buy a ticket from the automatic ticket machines or the news agents (cost: 1 euro). At Anagnina Station, take the subway A to Roma Termini Station. Once at Termini Station, you can reach ISTC-CNR on foot (10 minutes away, see map below) or take the subway. Otherwise, switch subway line to subway B following the signs for “Metro B, direzione Rebibbia”. Get off after one stop at subway stop “Castro Pretorio”. ISTC-CNR, a brown historical two-floor building, is round the corner of this subway stop at the beginning of Via S. Martino della Battaglia, at the entrance number 44 (see map below).
Taxis from Ciampino airport to ISTC-CNR charge about 30 euros, and take about 20 minutes to get there.


MAPS
Rome and the two airports: ISTC-CNR is situated at the centre of Rome

 

ISTC-CNR at Via San Martino della Battaglia 44, one subway stop from central Termini Station (subway B, stop Castro Pretorio)

 

HOTELS

Hotels close to ISTC-CNR
Hotel Villafranca (4 stars), Via Villafranca 9, tel: +39-06-4440364
Champagne Hotel (4 stars), Via Vittorio Bachelet 4, tel: +39-06 -927209 or +39-06-492721
Artdeco Hotel (4 stars), Via Palestro 19, tel: +39-06-4457588
Hotel S. Marco (3 stars), Via Villafranca 1, tel: +39-06-490437
Hotel Piemonte (3 stars), Via Vicenza 32/a, tel: +39-06-4452240
Hotel Montecarlo (3 stars), Via Palestro 17/a, tel: +39-06-4460000
Hotel Astoria, (3 stars), Via Vittorio Bachelet 8, tel: +39-06-4469908
Hotel Lux (3 stars), Via Gaeta 14, tel: +39-06-4441692
Hotel Brasile (3 stars), Via Palestro 13, tel: +39-06-4819486
Hotel Villa delle Rose srl (3 stars), Via Vicenza 5, tel: +39-06-4451795
Hotel Dolomiti - Sada sas (3 stars), Via San Martino della Battaglia 11, tel: +39-06-491058 or +39-06-4957256
Hotel Fiamma (3 stars), Via Gaeta 61, tel: +39-06-4818436 or +39-06-4818912
Hotel Siviglia (3 stars), Via Gaeta 12, tel: +39-06-4441197 or +39-06-4441198
Windrose Hotel (3 stars), Via Gaeta 39, tel: +39-06-4821913
Hotel Fiume (3 stars), Via Brescia 5, tel: +39-06-8543000
Hotel Sunrise (3 stars), Via Cilento 3, tel: +39-06-82011093
Hotel Virginia (2 stars), Via Montebello 94, tel: +39-06-4457689
Hotel Mirage (2 stars) , Via Milazzo 6, tel: +39-06-4455661 or +39-06-4463124
Hotel Marco Polo (2 stars), Via Magenta 39, tel: +39-06-44704478 or +39-06-4474091
Solomon Hotels (2 stars), Via Palestro 9, tel: +39-06-4465890, +39-06-44703927 or +39-06-484940
Hotel dell'Urbe (2 stars), Via dei Mille 27/a, tel: +39-06-4455767
Hotels in the historic centre of Rome
Grand Hotel Plaza (5 stars) Via del Corso 126, tel +39-06-69921111, +39-06-69941575
Hotel Colonna Palace (4 stars) P. Monte Citorio 12, tel. +39-06-675191
Hotel Piranesi (4 stars) Via del Babbuino 196, tel +39-06-328041
Hotel Carriage (3 stars) Via delle Carrozze 36, tel. +39-06-6990124
Hotel del Corso (3 stars) Via del Corso, 79, tel. +39-06-36006233, 06-36006041
Hotel Madrid (3 stars) Via M. de Fiori 93-95, tel +39-06-6991510

Hotels in the historic centre of Rome
Grand Hotel Plaza (5 stars) Via del Corso 126, tel +39-06-69921111, +39-06-69941575
Hotel Colonna Palace (4 stars) P. Monte Citorio 12, tel. +39-06-675191
Hotel Piranesi (4 stars) Via del Babbuino 196, tel +39-06-328041
Hotel Carriage (3 stars) Via delle Carrozze 36, tel. +39-06-6990124
Hotel del Corso (3 stars) Via del Corso, 79, tel. +39-06-36006233, 06-36006041
Hotel Madrid (3 stars) Via M. de Fiori 93-95, tel +39-06-6991510