Computer Vision in Human-Robot Collaborative factories of the future (CVinHRC 2021) Call for Papers



ICCV 2020 Workshop on: Computer Vision in Human-Robot Collaborative factories of the future (CVinHRC 2021)


In Conjunction with ICCV 2021 - International Conference on Computer Vision

11-17 October 2021, Montreal, Canada


The workshop, as well as ICCV 2021, will be a virtual experience


Scope and Topics Covered

The technological breakthrough in robotics and the needs of the
factories of future (Industry 4.0) bring the robots out of their cages
to work in close collaboration with humans, aiming to increase
productivity, flexibility and autonomy in production. To enable true
and effective human-robot collaboration, the perception system of such
collaborative robots should be endorsed with advanced computer vision
methods that will transform them into active and effective co-workers.

Recent advances in the field of computer vision are anticipated to
resolve several complex tasks that require human-robot collaboration
in manufacturing and logistics domains. However, the applicability of
existing computer vision techniques in such factories of the future is
hindered from the challenges that real, unconstrained industrial
environments with cobots impose, such as variability in position and
orientation of manipulated objects, deformation and articulation,
existence of occlusions, motion, dynamic environments, human presence
and more.

In particular, the variability of manufactured parts and the lighting
conditions in realistic environments renders robust object recognition
and pose estimation challenging, especially when collaborative tasks
demand dexterous and delicate grasping of objects. Deep learning can
further advance the existing methods to cope with occlusions and other
incurred challenges, while also the combination of learning with
visual attentional models could reduce the need for data redundancy by
selecting most prominent and rich-in-context viewpoints to be
memorized, boosting the overall performance of the vision
systems. Moreover, close distance collaboration with humans requires
accurate SLAM and real time monitoring and modelling of the human body
to be applied for robot manipulation and AGV navigation tasks in
unconstrained environments, ensuring safety and human faith to the new
automation solutions. Alongside, further advanced semantic SLAM
methods are needed to endorse cobots with robust long-term autonomy
with no or minimal human intervention. What is more, the fusion of
deep learning with multimodal perception can offer solutions to
complex manufacturing tasks that require powerful vision systems to
deal with challenges such as articulated objects and deformable
materials handled by the robots. This can be achieved not only by
using vision systems as passive observers of the scene, but also with
the active involvement of the collaborative robots endorsed with
visual searching and view planning capabilities to drastically
increase their knowledge for their surroundings.

The goal of this workshop is to bring together researchers from
academia and industry in the field of computer vision and enable them
to present novel methods and approaches that set the basis for further
advanced robotic perception dealing with the significant challenges of
human robot collaboration in the factories of future.


We encourage submissions of original and unpublished works that
address computer vision for robotic applications in manufacturing and
logistics domain, including but not limited to the following:

        Deep learning for object recognition and pose estimation in manufacturing and logistics
        6-DoF object pose estimation for grasping
        Real time object tracking and visual servoing
        Vision-based object affordances learning
        Vision-based manipulation skills modelling and knowledge transfer
        View planning with robot active vision
        Human presence modelling, detection and tracking in real factory environments
        Human-robot workspace modelling for safe manipulation
        Semantic SLAM and lifelong environment learning
        Safe AGV navigation based on visual input
        Multi-AGVs perception and coordination for multiple tasks
        Visual search for AGVs and manipulators in industrial environments
        Sensor fusion (Camera, Lidar, Haptic, etc.) for enhanced scene understanding
        Vision-based attention modeling for collaborative tasks

Invited Speakers

*         Prof. Lydia Kavraki, Rice University, USA

*         Prof. John Tsotsos, York University, Canada

*         Prof. Markus Vincze, Technical University of Vienna, Austria

*         Prof. Danica Kragic, Royal Institute of Technology, KTH, Sweden

*         Prof. Antonios Argyros, University of Crete, Greece

*         Dr. Georgia Gkioxari, Facebook Research


Important Dates

Paper Submission Deadline:           July 2, 2021

Author Notification:                         July 23, 2021

Camera Ready Submission:            August 1, 2021


Workshop Paper Submissions

Conference papers will be submitted electronically through the
workshop submission service website
(, in PDF format.

Papers should be properly anonymized and should follow the guidelines
and template of ICCV 2021:

For further information on the papers submission process, please visit
the workshop website:


Workshop Organizers

Dimitrios Giakoumis, Senior Researcher, Grade C' at CERTH/ITI,
Ioannis Kostavelis, Senior Researcher, Grade C' at CERTH/ITI,

Ioannis Mariolis, Postdoctoral Research Associate at CERTH/ITI,
Dimitrios Tzovaras, Senior Researcher, Grade A' at CERTH/ITI and CERTH
President of the Board,


Dr. Dimitrios Giakoumis

Electrical and Computer Engineer, M.Sc., PhD.

Senior Researcher (Grade Cí)


Information Technologies Institute (ITI)

Centre for Research and Technology Hellas

6th Km Charilaou-Thermi Road

57001 (PO Box 60361)

Thermi-Thessaloniki, Greece

Tel. : +30 - 2311 - 257 707

Fax : +30 - 2310 - 474 128