2011

Funding: European Commission (FP7-ICT-270460)

Partners: Politecnico di Milano, Italy; Karlsruher Institut fuer Technologie, Germany; Deutsches Forschungszentrum fuer Kuenstliche Intelligenz GMBH, Germany; Medimaton Limited, United Kingdom; Force Dimension S.A.R.L., Switzerland' Fondazione IRCCS Istituto Neurologico Carlo Besta, Italy; Consiglio Nazionale delle Ricerche, Italy; CF Consulting Finanziamenti Unione Europea SRL, Italy; The Foundation for Medical Research, infrastructural Development and Health Services next to the Medical Center Tel Aviv, Israel; Renishaw LTD, Ireland; KUKA Roboter GMBH, Germany; Imperial College of Science, Technology and Medicine, United Kingdom; Fondazione Istituto Italiano di Tecnologia, Italy; Technische Universitaet Muenchen, Germany; TECHNION, Israel Institute of Technology, Israel.

Duration: 1 April 2011 – 31 March 2015.

Description: The ACTIVE project exploits ICT and other engineering methods and technologies for the design and development of an integrated redundant robotic platform for neurosurgery. A light and agile redundant robotic cell with 20 degrees-of-freedom (DoFs) and an advanced processing unit for pre- and intra-operative control will operate both autonomously and cooperatively with surgical staff on the brain, a loosely structured environment. As the patient will not be considered rigidly fixed to the operating table and/or to the robot, the system will push the boundaries of the state of the art in the fields of robotics and control for the accuracy and bandwidth required by the challenging and complex surgical scenario.

Two cooperating robots will interact with the brain that will deform for the tool contact, blood pressure, breathing and deliquoration. Human factors are considered by allowing easy interaction with the users through a novel haptic interface for tele-manipulation and by a collaborative control mode ("hands-on"). Force and video feedback signals will be provided to surgeons. Active constraints will limit and direct tool tip position, force and speed preventing damage to eloquent areas, defined on realistic tissue models updated on-the-field through sensors information. The active constraints will be updated (displaced) in real time in response to the feedback from tool-tissue interactions and any additional constraints arising from a complex shared workspace.

The overarching control architecture of ACTIVE will negotiate the requirements and references of the two slave robots. The operative room represents the epitome of a dynamic and unstructured volatile environment, crowded with people and instruments. The workspace will thus be monitored by environmental cameras, and machine learning techniques will be used for the safe workspace sharing. Decisions about collision avoidance and downgrading to a safe state will be taken autonomously, the movement of the head of the patient will be filtered by a bespoke active head frame, while fast and unpredictable patient motion will be compensated by a real-time cooperative control system. Cognitive skills will help to identify the target location in the brain and constrain robotic motions by means of on-field observations.

URL: http://www.active-fp7.eu

Funding: European Commission (FP7-ICT-270382)

Partners: Universität Graz, Austria; University of York, United Kingdom; Scuola Superiore di Studi Universitari e di Perfezionamento Sant'Anna, Italy; Universität Stuttgart, Germany; Université Libre de Bruxelles, Belgium.

Duration: 1 April 2011 – 31 March 2014.

Description: This project aims at creating a swarm of interacting, cognitive, autonomous robots. We will develop a swarm of autonomous underwater vehicles (AUVs) that are able to interact with each other and which can balance tasks (interactions between/within swarms). These tasks are: ecological monitoring, searching, maintaining, exploring and harvesting resources in underwater habitats. The swarm will maintain swarm integrity under conditions of dynamically changing environments and will therefore require robustness and flexibility. This will be achieved by letting the AUVs interact with each other and exchange information, resulting in a cognitive system that is aware of its environment, of local individual goals and threats and of global swarm-level goals and threats. Our consortium consists of both, biological and technical institutions and is therefore optimally qualified to achieve this goal.

By a combination of locally acting and globally acting self-organizing mechanisms, information from the global level flows into the local level and influences the behaviour of individual AUVs. Such a cognitive-based scheme creates a very fast reaction of the whole collective system when optimizing the global performance. As shown by natural swimming fish swarms, such mechanisms are also flexible and scalable. The usage of cognition-generating algorithms can even allow robotic swarms to mimic each other's behaviour and to learn from each other adequate reactions to environmental changes. In addition, we plan to investigate the emergence of artificial collective pre-consciousness, which leads to self-identification and further improvement of collective performance. In this way we explore several general principles of swarm-level cognition and can assess their importance in real-world applications. This can be exploited for improving the robustness, flexibility and efficiency of other technical applications in the field of ICT.

URL: http://cocoro.uni-graz.at

Funding: European Commission (FP7-ICT-270490)

Partners: Universitat Pompeu Fabra, Spain; Institut National de la Santé et de la Recherche Médicale (INSERM), France; Imperial College of Science, Technology and Medicine, United Kingdom; University of Sheffield, United Kingdom; Fondazione Istituto Italiano di Tecnologia, Italy.

Duration: 1 January 2011 – 31 December 2013.

Description: As the introduction of robots into our daily life becomes a reality, the social compatibility of such robots gains importance. In order to meaningfully interact with humans, robots must develop an advanced real-world social intelligence that includes novel perceptual, behavioural, emotional, motivational and cognitive capabilities. The Experimental Functional Android Assistant (EFAA) project will contribute to the development of socially intelligent humanoids by advancing the state of the art in both single human-like social capabilities and in their integration in a consistent architecture. The EFAA project proposes a biomimetic, brain-inspired approach. The central assumption of EFAA is that social robots must develop a sense of self as to overcome the fundamental problem of social inference. It only in possesses the core aspects of a human-like self, that inferences about others can be made through analogy.

The EFAA Biomimetic Architecture for Situated Social Intelligence Systems, called BASSIS, is based on our growing understanding of the neuronal mechanisms and psychological processes underlying social perception, cognition and action and will exploit the availability, amongst the members of the consortium, of a number of complementary prototype robot-based perceptual, cognitive and motor architectures. By integrating across these existing architectures, by directing focused effort on specific core problems, and by exploiting the availability of unique advanced real-time neuronal simulation and hardware, the impact of the EFAA project is assured. The EFAA project will apply and benchmark BASSIS on a mobile humanoid assistant based on the iCub platform. The resultant system, EFAA, will actively engage in social interactions and interactive cognitive tasks. To facilitate the realization of these game alike interactions and the performance analysis will be facilitated through a mixed-reality interaction paradigm using a table-top tangible interface system.

URL: http://efaa.upf.edu/project

Funding: European Commission (FP7-ICT-270182)

Partners: Universität Bielefeld, Germany; Syddansk Universitet, Denmark; Universitá degli Studi di Catania, Italy; Johannes Gutenberg Universität Mainz, Germany.

Duration: 1 February 2011 – 31 January 2013.

Description: The EMICAB consortium takes a holistic approach to the engineering of artificial cognitive systems. Our goal is to integrate smart body mechanics in intelligent planning and control of motor behaviour. To achieve this goal the consortium accounts equally for problems in neuroscience (e.g., multi-sensory integration, internal body models, intelligent action planning) and technology (smart body mechanics, distributed embodied sensors and brain-like controllers).

Our approach starts with a strongly sensorised bionic body with redundant whole-body kinematics and then designs the technological infrastructure such that cognitive mechansims emerge from distributed sensorimotor intelligence. The concept is based on neuroscience research on insects whose motor dexterity, adaptiveness and pre-rational abilities in learning and memory rival those of lower mammals: stick insects orchestrate a wide range of dexterous motor behaviours and flies can maintain object locations in short-term memory during navigation tasks, just to mention paradigms that are studied by UNIBI and JGUM.

The partners UNICT and SDU will devise bio-inspired models and, in turn, guide ongoing experimental research in order to achieve the overall technological goal: a dexterous hexapod robot that exploits its bodily resources for cognitive functions. Two levels of analysis and modelling will be accounted for: the smart brain that captures various aspects of motion intelligence (motor learning, context-dependent actions, multi-sensory integration) and the smart body equipped with distributed proprioceptors and muscle-like compliance, allowing for novel, highly adaptive, neurobionic control strategies. The EMICAB robot will draw from its complex body features and learn by use of a useable internal body model. This will be monitored by an ambitious set of benchmarking scenarios. We expect mutual benefit for applied research on autonomous mobile robots and for basic research in neuroscience.

URL: http://www.emicab.eu

Funding: European Commission (FP7-ICT-269959)

Partners: Syddansk Universitet, Denmark; Rheinisch-Westfaelische Technische Hochschule Aachen, Germany; Georg-August-Universität Goettingen, Germany; Jozef Stefan Institute, Slovenia; Universität Innsbruck, Austria; Agencia Estatal Consejo Superior de Investigaciones Cientificas, Spain.

Duration: 1 March 2011 – 28 February 2014.

Description: IntellAct addresses the problem of understanding and exploiting the meaning (semantics) of manipulations in terms of objects, actions and their consequences for reproducing human actions with machines. This is in particular required for the interaction between humans and robots in which the robot has to understand the human action and then to transfer it to its own embodiment. IntellAct will provide means to allow for this transfer not by copying movements of the human but by transferring the human action on a semantic level. IntellAct will demonstrate the ability to understand scene and action semantics and to execute actions with a robot in two domains. First, in a laboratory environment (exemplified by a lab in the International Space Station (ISS)) and second, in an assembly process in an industrial context.

IntellAct consists of three building blocks: (1) Learning: Abstract, semantic descriptions of manipulations are extracted from video sequences showing a human demonstrating the manipulations; (2) Monitoring: In the second step, observed manipulations are evaluated against the learned, semantic models; (3) Execution: Based on learned, semantic models, equivalent manipulations are executed by a robot.

The analysis of low-level observation data for semantic content (Learning) and the synthesis of concrete behaviour (Execution) constitute the major scientific challenge of IntellAct.

Based on the semantic interpretation and description and enhanced with low-level trajectory data for grounding, two major application areas are addressed by IntellAct: First, the monitoring of human manipulations for correctness (e.g., for training or in high-risk scenarios) and second, the efficient teaching of cognitive robots to perform manipulations in a wide variety of applications.

To achieve these goals, IntellAct brings together recent methods for (1) parsing scenes into spatio-temporal graphs and so-called semantic Event Chains , (2) probabilistic models of objects and their manipulation, (3) probabilistic rule learning, and (4) dynamic motion primitives for trainable and flexible descriptions of robotic motor behaviour. Its implementation employs a concurrent-engineering approach that includes virtual-reality-enhanced simulation as well as physical robots. Its goal culminates in the demonstration of a robot understanding, monitoring and reproducing human action.

URL: http://www.intellact.eu

Funding: European Commission (FP7-ICT-270435)

Partners: The University of Edinburgh, United Kingdom; Fortiss GMBH, Germany; Foundation for Research and Technology Hellas, Greece; Universität Bielefeld, Germany; Heriot-Watt University, United Kingdom.

Duration: 1 February 2011 – 31 January 2014.

Description: The JAMES project aims to develop a socially intelligent humanoid robot combining efficient task-based behaviour with the ability to understand and respond in a socially appropriate manner to a wide range of multimodal communicative signals in the context of realistic, open-ended, multi-party interactions.

To direct our research in JAMES, we will focus on five core objectives:

1. analysing natural human communicative signals,
2. building a model of social interaction,
3. extending the model to manage learning and uncertainty,
4. implementing the model on a physical robot platform, and
5. evaluating the implemented system.

The work in JAMES will build on state-of-the-art results and techniques in seven areas: social robotics, social signal processing, machine learning, multimodal data collection, planning and reasoning, visual processing, and natural language interaction.

JAMES will combine the analysis of human social communicative behaviour, the development and integration of state-of-the-art technical components, and the evaluation of integrated systems. Work on these threads will be interleaved: the results of the human data analysis will be used in the development of the technical components, while the robot will be used for further data collection and evaluation studies.

JAMES will extend the state-of-the-art in social robotics by moving beyond one-on-one, long-term relationships to deal with more open-ended, multi-party, short-term situations. The research in JAMES will also increase our understanding of how humans use multimodal social cues to communicate and coordinate their interactions in task-driven, joint-action contexts. The individual technical contributions to the system components will also provide state-of-the-art results in their respective research areas.

URL: james-project.eu

Funding: European Commission (FP7-ICT-270247)

Partners: Ruhr-Universität Bochum, Germany; Hogskolan I Skovde, Sweden; CINTAL - Centro Investigacao Tecnologica do Algarve, Portugal; Scuola Universitaria Professionale della Svizzera Italiana (SUPSI), Switzerland.

Duration: 1 April 2011 – 31 March 2015.

Description: Endowing robots with cognition is a long-standing and difficult objective. Substantial progress in cognitive science and neuroscience has led to the insight that cognition is tightly linked to the sensory and motor surfaces, and that cognition emerges during development from relatively low-level mechanisms when situated in a structured environment.

Building on basic functions such as detection and selection, we will develop a set of elements of cognition and techniques for combining such elements, allowing us to scale towards such cognitive capabilities as scene representation and sequence generation. We will implement and evaluate these elements of cognition in scenarios inspired by the development of cognition in early childhood.

URL: http://www.neuraldynamics.eu

Funding: European Commission (FP7-ICT-270350)

Partners: Fachhochschule Reutlingen, Germany; Berthold Vollers GMBH, Germany; Qubiqa A/S, Denmark; Universitá di Pisa, Italy; Jacobs University Bremen, Germany; Bremer Institut fuer Produktion und Logistik GMBH, Germany; Orebro University, Sweden.

Duration: 1 April 2011 – 31 March 2015.

Description: Globalization causes an increasing transport of goods. Nowadays, most goods are shipped in containers and are transferred onto trucks for further transport. The containers are unloaded manually since they are nearly always packed chaotically, the variety of transported goods is high, and time requirements are strict. Unloading of containers is a strenuous task as goods have a weight up to 70 kg that poses health risks, which include the effects of pesticides and poisonous gases as well as injuries through unexpectedly falling objects. Human labour is hence a high cost factor combined with unhealthy working conditions, making automated solutions highly desirable. Existing systems for automated unloading are restricted to specific scenarios and still have drawbacks in their flexibility, adaptability and robustness. A robotic system suited for any unloading task of containers requires a high amount of cognitive capabilities.

RobLog aims at developing appropriate methods and technologies meeting the requirements to automate logistics processes. The RobLog system has to be capable of 3D perception in a challenging scenario (high variability of objects, dynamic scene, deformable objects). The perceived environment has to be integrated into a 3D model in real-time. Grasping hypotheses, decisions and path-plans have to be generated and executed in an adaptive manner including obstacle avoidance and re-planning, if necessary. The actions must be grounded in a physical set-up capable of handling a large variety of potentially deformable items. Finally, there is the need to provide an interface suited for a human operator to provide high-level instructions to a multitude of systems operating at several unloading docks in parallel. All these advances are demonstrated within the project in close cooperation with an industrial end-user in a realistic application scenario; thus opening the potential to reach a completely new level of automation in the logistics chain.

URL: http://www.roblog.eu

Funding: European Commission (FP7-ICT-270436)

Partners: Kungliska Tekniska Högskolan, Sweden; University of Edinburgh, United Kingdom; King's College London, United Kingdom; Freie Universität Berlin, Germany; Universidad Granada, Spain.

Duration: 1 April 2011 – 31 March 2014.

Description: The aim of TOMSY is to enable a generational leap in the techniques and scalability of motion synthesis algorithms. We propose to do this by learning and exploiting appropriate topological representations and testing them on challenging domains of flexible, multi-object manipulation and close contact robot control and computer animation.

Traditional motion planning algorithms have struggled to cope with both the dimensionality of the state and action space and generalisability of solutions in such domains. This proposal builds on existing geometric notions of topological metrics and uses data driven methods to discover multi-scale mappings that capture key invariances - blending between symbolic, discrete and continuous latent space representations. We will develop methods for sensing, planning and control using such representations.

TOMSY, for the first time, aims to achieve this by realizing flexibility at all the three levels of sensing, representation and action generation by developing novel object-action representations for sensing based on manipulation manifolds and refining metamorphic manipulator design in a complete cycle. The methods and hardware developed will be tested on challenging real world robotic manipulation problems ranging from primarily 'relational' block worlds, to articulated carton folding or origami and all the way to full body humanoid interactions with flexible objects.

The results of this project will go a long way towards providing some answers to the long standing question of the 'right' representation in a sensorimotor control and provide a basis for a future generation of robotic and computer vision systems capable of real-time synthesis of motion that result in fluent interaction with their environment.


URL: http://www.tomsy.eu

Funding: European Commission (FP7-ICT-270273)

Partners: Karlsruher Institut fuer Technologie, Germany; Georg-August-Universität Goettingen, Germany; The University of Edinburgh, United Kingdom; Syddansk Universitet, Denmark; Jozef Stefan Institute, Slovenia; Universität Innsbruck, Austria; Fondazione Istituto Italiano di Tecnologia, Italy.

Duration: 1 January 2011 – 31 December 2015.

Description: Current research in embodied cognition builds on the idea that physical interaction with and exploration of the world allows an agent to acquire intrinsically grounded, cognitive representations which are better adapted to guiding behaviour than human crafted rules. Exploration and discriminative learning, however, are relatively slow processes. Humans are able to rapidly create new concepts and react to unanticipated situations using their experience. They use generative mechanisms, like imagining and internal simulation, based on prior knowledge to predict the immediate future. Such generative mechanisms increase both the bandwidth and speed of cognitive development, however, current artificial cognitive systems do not yet use generative mechanisms in this way.The Xperience project addresses this problem by structural bootstrapping, an idea taken from language acquisition research: knowledge about grammar allows a child to infer the meaning of an unknown word from its grammatical role together with understood remainder of the sentence.

Structural bootstrapping generalizes this idea for general cognitive learning: if you know the structure of a process the role of unknown actions and entities can be inferred from their location and use in the process. This approach will enable rapid generalization and allow agents to communicate effectively. Xperience will implement a complete robot system for automating introspective, predictive, and interactive understanding of actions and dynamic situations based on structural bootstrapping. Xperience will evaluate and benchmark this on state-of-the-art humanoid robots demonstrating rich interactions with humans. By equipping embodied artificial agents with the means to exploit prior experience via generative inner models, XPERIENCE will have a major impact in a wide range of autonomous robotics applications that benefit from efficient learning through exploration, predictive reasoning and external guidance.

URL: http://www.xperience.org