Senior researcher at INRIA Paris and head of scientific board at VisionLabs, holds PhD degree from the Royal Institute of Technology (2004) and HDR degree from Ecole Normale Superieure (2013). Recipient of an ERC Starting Grant (2012) and an ICCV Helmholtz prize (2017). Program chair for CVPR (2018) and associate editor of IJCV and TPAMI journals.
Topics of interest
Computer vison, robotics, machine learning
Project in Prairie
Ivan Laptev will address the synergy between computer vision, robotics and natural language understanding. He will focus on learning embodied visio-linguistic representations for robotics exploring methods such as deep imitation, reinforcement learning and weakly-supervised learning for transferring knowledge from human demonstrations and instructions. He will collaborate with industrial partners and teach classes at the MVA Mater program.
Deep neural networks and machine learning have recently revolutionized computer vision. Typical proxy tasks such as object recognition and semantic image segmentation have achieved maturity. Yet, this progress so far had only limited influence on robotics. While perception and vision in particular are crucial components of robotics, traditional robotics methods typically decouple perception from control. With the advances in deep learning, an integrated approach of learning visual representations together with control functions now gives an opportunity for a breakthrough in the field. My goal in PRAIRIE is to bring advances of computer vision and natural language processing to robotics. While supervised learning has been crucial for the progress in computer vision, full supervision is rarely available for robotics tasks. Overcoming this limitation will be a major scientific challenge of the project.