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Enrica Zereik

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Contact information

Position Senior Research Scientist
Phone +39 010 6475641
Email
OfficeGenoa
AddressVia de Marini 16, 16149 Genoa, Italy
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Short biography

Enrica Zereik was born in Genova on the 2nd of May 1982, and graduated in Computer Engineering at the University of Genova on the 27th October 2004 (bachelor degree) and on 3rd November 2006 (master degree). On 7th April 2010 she discussed her Ph.D. thesis on “Space Robotics Supporting Exploration Missions: Vision, Force Control and Coordination Strategies” at the University of Genova and in 2012 she joined the Italian National Research Council (CNR). She currently is a Senior Research Scientist at CNR-INM, focusing her research activity on underwater manipulation, computer vision, advanced algorithms for navigation guidance and control, evaluation indices & metrics for the experimental assessment of marine platforms’ performance, coordination and control algorithms for cooperative multi-robot systems, experimental reproducibility. She is CNR Head of the Joint Lab heron@cnr, and she coordinates the BEASTIE project. She serves as Editor for IEEE RA-M. She cooperates in many different national and international projects and she is co-authors of several scientific publications, in relevant international journals and conferences.

Research interests

Her research interests range within different topics in the context of robotics and control. In the first part of her career, she mainly focused on Space Robotics, working at Thales Alenia Space in Turin during her Ph.D. on an ESA (European Space Agency) project aimed at developing an autonomous robotic crew assistant for astronauts in planetary exploration missions. Many skills and expertise were involved: from advanced manipulation and control, to macro-structure cooperation, robot coordination and artificial vision.
After joining CNR, she focused on marine robotics, dealing with many different aspects: from advanced navigation guidance and control systems and underwater/in-air robust manipulation, to stochastic control techniques, computer vision and perception in unstructured environments, to coordination and control algorithms for cooperative multi-robot systems, and the use of machine learning strategies for autonomous robots.
Recently, she focused on the design of reproducible experiments, and on the definition and employment of indices and metrics for the experimental evaluation of robot performance within marine applications. Reproducibility is a very strong and important topic that will help the real technology transfer from robotic prototypes to industry and applications. This is true especially in a field like marine robotics, where there is the strong need to experimentally verify and repeatably validate the approaches that arise in the literature, to be able to apply them in the relevant environment.
She participated in several European and National research projects, collaborating with several national and international research institutions. She currently is the coordinator of the Italian PRIN (Research Program of National Interest) project BEASTIE (roBotic undErwater Autonomous Social Team for cooperative manipulation and IntelligencE), aiming at realizing a disruptive new concept in underwater manipulation, based on cooperation of many small little robots equipped with grippers but without arms. Recently, she was the PI for CNR of the project HumaBeliefs (Benchmarking Humanoid Belief Space Locomotion Planning, funded within the FSTP-2 Open call of the European H2020 EUROBENCH project), aiming at making humanoid robots keep balance while walking like in humans.
Since 2020 she is the CNR Head of the heron@cnr Joint Lab, an innovative academia-industry collaboration on robotics and AI related research and innovation activities.
She served as Associate Editor for IEEE Robotics and Automation Magazine (since 1st January 2021), and she currently became Editor for IEEE Robotics and Automation Magazine (since 1st January 2025). She has been Associate Editor for the International Conference on Robotics and Automation in 2024 and 2025, and had many other editorial responsibilities (Special Issues on IFAC Annual Reviews in Control, on Frontiers in Robotics and AI, and invited tracks and sessions in many different IFAC and IEEE conferences).
She co-authored about 100 papers in international journals and conferences. In 2017 she was awarded the IEEE Transactions on Control Systems Technology Outstanding Paper Award.
She participated as panelist and invited speaker in many different workshops, tutorials, series of lectures in relevant international events such as ICRA, IROS, ERF, RoboSoft, ShanghAI lectures.
She is currently a member of the IFAC Technical Committee on Marine Systems (TC 7.2), and IEEE Member.

Research topics/groups

Underwater manipulation
Computer vision
Advanced algorithms for navigation guidance and control
Evaluation indices and metrics for the experimental assessment of robotics performance
Coordination and control algorithms for cooperative multi-robot systems
Experimental reproducibility

Selected projects

BEASTIE – roBotic undErwater Autonomous Social Team for cooperative manipulation and IntelligencE
The BEASTIE project aims at pursuing a new, disruptive concept in underwater soft manipulation for a wide range of applications. The BEASTIE concept eliminates the constraints given by cooperation of multiple physical kinematic chains focusing on the issue of controlling a morphing multi-body supra-manipulator constituted by single-link agents connected through virtual links.
The manipulation capability is pursued by a team of small floating robots, endowed with 6 DoF-motion skills in water, able to communicate and cooperate together to accomplish the desired task. Each robot is constituted by a very simple soft gripper and a body, equipped with suitable actuators and sensors to support its 6 DoFs motion in the underwater space (complete attitude control), with no kinematic chains or joints in-between. The robot body should be able to: i) reach the target object; ii) suitably orient the gripper to allow grasping and manipulation of objects placed in the whole 3d space, i.e. not only laying on the seabed but also on rocks, cliffs or inside small underwater caves.
A hybrid layered approach is being adopted integrating, in a complementary way, distributed impedance control for grasping, Belief Space Planning for collective movement of the agents and Deep Learning for control optimization and object recognition. The gripper has a camera in the palm, and is built with soft materials to gain compliance in order to guarantee the preservation of fragile samplings. Softness also improves grasping stability by adapting the grippers of the different robots during the cooperative manipulation task.
Research about smart materials is developing innovative coatings to gain high performance in robot motion and, at the same time, optimize the energy management onboard. The team is able to constitute a morphing multi-body supra-manipulator in which the links among the single agents are not physical but only virtual (through information exchanges). Given the short range in which the different robots should operate, the possibility to adopt faster electromagnetic communication among close agents is being investigated.
Cooperation is enhanced by a social behavior skill that will allow robots to predict their companions’ behavior to be robust to unexpected events and failures, developing a sort of collective intelligence. The real deployment and validation of cooperative/social models is still an open challenge. This project aims to leverage the design and development of a social model that it is inspired to human-human and human-robot interaction to enhance the technical reliability of the proposed system as well as to optimize the performance of the system.
With its analogies with collective manipulation and transport strategy for large and heavy objects typical of ant colonies and the capability of “flying” (in water) in all the directions of motion, the BEASTIE project paves the way to further investigations in bio-inspired robotics.
HumaBeliefs – Belief Space Planning for Robust Humanoid Locomotion
The HumaBeliefs project has implemented a BSP (Belief Space Planning) controller inspired by human walking behaviour’s neurophysiological mechanisms on the PAL Robotics REEM-C humanoid robot. BSP controllers assuming maximum likelihood have been previously successfully utilized by our group for various similar demanding applications. The posture and locomotion performance of the REEM-C robot with BSP controllers has been experimentally
tested and validated in the Eurobench@IIT humanoid testing facility by means of the benchmarking framework and software already developed in EU-funded Eurobench project. The main purpose of the activity carried out in the context of HumaBeliefs is to assess if and how the application of a BSP controller is viable and useful in the humanoid robotics domain. The BSP walking controller has shown remarkably good performance in tracking tasks. However, it has not performed well on slopes in simulation. A new more advanced approach aiming to cope with slopes and unstable terrain is under development.

Selected publications

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