Robots have been able to serve the original promise to replace human counterparts in laborious, hazardous, and repetitive tasks mainly in the area of position control that includes tasks such as pick and place of components, arc welding, grinding known objects, and even in bipedal walking on fairly smooth and known grounds. However, robots still find it hard to carry out stable force control tasks on uncertain objects or walk on natural soft terrains (grass, sand, mud). Just like the difference between the way we use the left hand and the right hand can not be explained using their biomechanical basis alone, the answer to robotic survival in uncertain environments does not come from an attempt to build robots that resemble human bodies alone. From early 1980s, scientists have begun to believe that the secrets of stable interactions with natural compliant environments will come from an ability of the robot itself to be compliant. The original work of Neville Hogan on impedance control was based on this concept. Since then, a considerable body of literature can be found on how impedance control is applied in various force control applications such as rehabilitation, massaging, bipedal walking, exoskeletal robotics, and several other direct interactions with humans. However, still there is no answer to how impedance control should be adaptively managed to sustain stability when the coupled dynamics between the robot and the environment evolves metastable dynamics. The theory of Metastability states that an uncertain dynamics system can exhibit intermittent instability though it may stay stable most of the time. A human using a screw driver is one example, where the dynamic contact with the screw may stay stable most of the time, but exhibit intermittent slipping due to uncertainty in the friction between the screw and the surrounding medium. Even a human walker can fall down in rare situations due to the same phenomenon. However, an uncertain dynamic system can enhance stability if it can predict where it is likely to fail. A number of recent advances in metastable systems use the concept of mean first passage time (MFPT) as an indicator to assess the current control policy in an uncertain environment. MFPT is the expected time to the next failure situation given the current knowledge of the uncertain dynamics of the coupled dynamics of the robot and the environment.Therefore, this project aims at developing a unifying theory of impedance control for robots that are in dynamic contact with uncertain environments. The generic method that can start to perform stable hybrid position/force control on an uncertain environment with partially known dynamics and recursively build a robust internal model to perform stable position/force control on an environment that changed its stiffness, viscosity, and inertia. Then an algorithm will be developed to use a locally linearised model of the above coupled dynamic system to estimate the MFPT of the robot and the environment. This MFPT will then be used in a novel real-time algorithm to adapt a bank of candidate impedance parameter sets and adaptively choose the best parameter set to suit the environment in order to maximise the MFPT. Rigorous theoretical proofs of stability and experimental validation of methods will be given. The project will use a custom built experimental platform to evaluate and refine the fundamental theories and algorithms that will be developed in this project. The PI will closely collaborate with Shadow Robotics Company, a UK based SME who develops biomimetic robotic hands, and the robotics group led by Professor Darwin Caldwell at the Italian Institute of Technology, where the researchers strive to enable the humanoid robot i-Cub to interact with natural uncertain environments. Therefore, this project will benefit from a wealth of experiences the collaborators have already gathered on real robots interacting with natural environments.

Superresolution encompasses a range of techniques for transcending the resolution limit of a sensor and earned the 2014 Nobel Prize in Chemistry (for superresolved fluorescence microscopy). Superresolution is analogous to biological hyperacuity of vision and touch where the discrimination is finer than the spacing between sensory receptors. Superresolution research in visual imaging has impacted science from cell biology to medical scanning 'in ways unthinkable in the mid-90s' (Editorial, Nature 2009). The success of this proposal will enable the widespread uptake of superresolution techniques in the domain of artificial tactile sensing, potentially impacting multiple application areas across robotics from autonomous quality control in manufacturing to sensorized grippers for autonomous manipulation to sensorized prosthetic hands and medical probes in healthcare. Proposed research More specifically, the development of robust and accurate artificial touch is required for autonomous robotic systems to interact physically with complex environments, underlying the future robotization of broad areas of manufacturing, food production, healthcare and assisted living that presently rely on human labour. Currently, there are many designs for tactile sensors and various methodologies for perception, from which general principles are emerging, such as taking inspiration from human touch (Dahiya et al 2012), using statistical approaches to capture sensor and environment uncertainty and combining tactile sensor control and perception (Prescott et al 2012). All application areas of robot touch are currently limited by the capabilities of tactile sensors. This first grant proposal aims to demonstrate that tactile superresolution can radically improve tactile sensor performance and thus potentially impact all areas of robotics involving physical interaction with complex environments. Visual superresolution has revolutionised the life sciences by enabling the imaging of nanoscale features within cells. Tactile superresolution has the potential to drive a step-change in tactile robotics, with applications from quality control and autonomous manipulators in manufacturing (Yousef et al 2011) to sensorized prosthetics and probes in healthcare. Proposed initial application domain Currently, across the entire automobile industry, gap and flush quality controls are made manually by human operators using their hands to check the alignment between vehicle parts. Experts in the industry have informed me that human hands are used because modern vision-based measuring technologies (such as laser scanners) do not robustly detect sub-millimetre misalignments between parts of differing reflectivity and refractivity. An automated system using robot touch would be more reliable, enable traceability of defects, and move production towards a fully automated paradigm. The proposed research will culminate in a pilot study demonstrating that tactile superresolution will enable readily available tactile sensors to make gap and flush measurements of the requisite sub-millimetre tolerance and how the sensors should be controlled during the tactile perception task. This will constitute a first step towards building a consortium between academic and industrial partners to develop a fully working prototype for test installation on a production line.

Grasping rigid objects has been reasonably studied under a wide variety of settings. The common measure of success is a check of the robot to hold an object for a few seconds. This is not enough. To obtain a deeper understanding of object manipulation, we propose (1) a task-oriented part-based modelling of grasping and (2) BURG - our castle* of setups, tools and metrics for community building around an objective benchmark protocol. The idea is to boost grasping research by focusing on complete tasks. This calls for attention on object parts since they are essential to know how and where the gripper can grasp given the manipulation constraints imposed by the task. Moreover, parts facilitate knowledge transfer to novel objects, across different sources (virtual/real data) and grippers, providing for a versatile and scalable system. The part-based approach naturally extends to deformable objects for which the recognition of relevant semantic parts, regardless of the object actual deformation, is essential to get a tractable manipulation problem. Finally, by focusing on parts we can deal easier with environmental constraints that are detected and used to facilitate grasping. Regarding benchmarking of manipulation, so far robotics suffered from uncomparable grasping and manipulation work. Datasets cover only the object detection aspect. Object sets are difficult to get, not extendible, and neither scenes nor manipulation tasks are replicable. There are no common tools to solve the basic needs of setting up replicable scenes or reliably estimate object pose. Hence, with the BURG benchmark we propose to focus on community building through enabling and sharing tools for reproducible performance evaluation, including collecting data and feedback from different laboratories for studying manipulation across different robot embodiments. We will develop a set of repeatable scenarios spanning different levels of quantifiable complexity that involve the choice of the objects, tasks and environments. Examples include fully quantified settings with layers of objects, adding deformable objects and environmental constraints. The benchmark will include metrics defined to assess the performance of both low-level primitives (object pose, grasp point and type, collision-free motion) as well as manipulation tasks (stacking, aligning, assembling, packing, handover, folding) requiring ordering as well as common sense knowledge for semantic reasoning.

The functional intricacy of the central nervous system (CNS) arises from the complex anatomical and dynamic interactions between different types of neurones involved in specific networks. Hence, the encoding of information in neural circuits occurs as a result of interactions between individual neurones as well as through the interplay within both microcircuits (made of few neurones) and large scale networks involving thousands to millions of cells. One of the great challenges of neuroscience nowadays is to understand how these neural networks are formed and how they operate. Such challenge can be resolved only through simultaneous recording from thousands of neurones that become active during specific neuronal tasks. One of the experimental approaches to fulfil this goal is to use multielectrode arrays (MEAs) that consist of several channels (electrodes) that can each record (and/or stimulate) from few adjacent neurones within a particular area of the CNS. MEAs can be used in vitro to record from dissociated neuronal cultures or from brain slices or isolated retinas. These MEAs consist of assemblies of electrodes embedded in planar substrates. Typical commercial MEAs consist of 60-128 electrodes with a spacing of 100-200 um. Considering that a generic neurone in the mammalian CNS has a diameter of about 10 um, it is obvious that such MEAs cannot convey information on the activity of all neurones involved in a specific network, but rather just from a sample of these cells. To overcome this activity under-sampling, in this project, we will use the Active Pixel Sensor (APS) MEA, a novel type of MEA platform developed in a NEST-EU Project by our collaborator Luca Berdondini (Italian Institute of Technology, Genova). This MEA consists of 4,096 electrodes with near cellular resolution (21x21 um, 42 um centre-to-centre separation, covering an active area of 2.5 mm x 2.5 mm), where recording is possible from all channels at the same time. We will use the APS MEA to record spontaneous waves of activity that are present in the neonatal vertebrate retina. These waves occur during a short period of development during perinatal weeks and they are known to play an important role in guiding the precise wiring of neural connections in the visual system, both at the retinal and extra-retinal levels. The APS-MEA, thanks to its unmet size and resolution, will enable us to reach new insights into the precise dynamics of these waves as never achieved before. Recordings from such large scale networks at near cellular resolution generate extremely rich datasets with the drawback that these datasets are very large and difficult to handle, thus necessitating the development of new powerful analytical tools enabling to decode in a fast, efficient and user-friendly way how cellular elements interact in the network. The development of such computational tools is the central goal of this project, while the experimental work on the retina defines a challenging and unique scientific context. The tools we plan to develop will yield parameters that will help us reach better understanding of network function, from the temporal firing patterns of individual neurones to how activity precisely propagates within the network. We will also develop novel tools for easier visualisation of the dynamical behaviour of the activity within the network. These tools will be developed in a language that could be easily utilized by other investigators using the same recording system or other platforms of their choice. Finally, to ensure that these tools are accessible to the wide neurophysiology community, they will be deployed on CARMEN (Code Analysis, Repository and Modelling for e-Neuroscience), a new internet-based neurophysiology sharing resource designed for facilitating worldwide communication between collaborating neurophysiologists.

The NOCODING-TRAIN consortium I am building aims at applying to the Innovative Training Networks (ITN) for European Training Networks (ETN) of the Marie Sklodowska-Curie Actions (MSCA) in January 2021. We will ask 540 person-months for a final budget of about 4.5 M Euros, which will be used to train 15 Ph.D. students in ten different European Institutions. To date, the consortium includes five partners coming from different European countries and Israel. I will look for five more partners having diverse experimental angles of ncRNA field to complete our multidisciplinary aspect. I will coordinate this project. Scientific project. Our project will focus on the emerging field of the noncoding transcripts. The recent discovery of the pervasive transcription of the noncoding regions of the eukaryotic genome has had a major impact on our understanding of gene expression regulation. In fact, noncoding RNAs (ncRNAs) originating from these regions are emerging as fundamental regulators of gene expression in both normal and pathological aspects of physiology, and act at multiple layers with effects on chromatin modifications, transcription, and RNA processing, turnover or translational rate. The ncRNAs use a wide variety of mechanisms to regulate gene expression programs in cells. The NOCODING-TRAIN consortium aims at applying to the MSCA-ITN grant by gathering teams working on different areas of ncRNA research field to train novel generations of scientists in this emerging field. Our training will focus on the control of the small and long ncRNA expression and their mode of action to regulate gene expression in eukaryotic cells during developmental, physiological, and pathological events. The NOCODING-TRAIN project is organized into 5 main axes, built on existing research forces and collaborative work from partners to address the following specific questions: 1. The molecular mechanisms regulating the expression of ncRNAs, with focus on biogenesis and turnover. 2. The cellular and subcellular heterogeneity of ncRNAs mode of action to regulate gene expression through epigenetic, transcriptional and post-transcriptional mechanisms. 3. ncRNAs in the pathogenesis of wide-spread human diseases, including the inheritance, onset and progression of cancer and cardiometabolic disorders. 4. Computational modeling of the expression control and mode of action of ncRNAs. 5. Economic valorization of the ncRNA research for therapeutic, diagnostic and prognostic improvement of human pathologies. Educational project. Attracting outstanding students represents a major challenge and a condition for high standard research. I propose an international Ph.D. training program, in which the experimental work in the laboratories will be coupled with training classes held by local as well as external experts in biology, bioinformatics, and biostatistics, but also in other disciplines including informatics, statistics, artificial intelligence scientific integrity, and scientific English classes. Workshops will be also organized twice a year to provide a stage to discuss individual Ph.D. student research projects and foster exchange between the laboratories. Providing interdisciplinary training is crucial to widen the technological and conceptual toolkit of young researchers, and a condition for innovative research in gene expression. According to the expertise of each partner of the consortium, we will provide specific experimental training. If successful, the MRSEI grant will be used to attend three scientific meetings to meet scientists in the different fields of ncRNA research, who can reinforce the multidisciplinary aspect of the consortium. I will organize 1 two-days symposium and 2 one-day round-table meetings with all proposed partners in Nice. I will use the MRSEI grant to make a website of the proposed training program, which will be accessible to the MSCA-ITN Reviewers, and publicly available only if our application will be successful.