The CVinHRC workshop is organized in conjunction with
ICCV 2021 - International Conference on Computer Vision, Montreal, Canada, October 11th - October 17th, 2021

The workshop will be held virtually, as ICCV 2021, on October 16th, 2021

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:

Invited Speakers :

Papers Submission Information

The review process for the submitted papers will be double-blind, and each submission will be reviewed by at least two reviewers. Papers that are not blind, or do not use the template, or have more than 8 pages will be rejected without review. Papers will be selected based on relevance, significance and novelty of results, technical merit, and clarity of presentation. All the accepted papers will be published in the ICCV workshop proceedings.

Important Dates


The organizers are currently participating in on-going H2020-funded research projects related to human robot collaboration, which support the workshop:

Workshop Schedule :

October 16, 13:00-18:00 EDT (Montreal time)