Postdoc Deep Learning for Human-Robot Interaction

This project will focus on various aspects of human robot interaction leveraging deep learning methods. Robots can learn to interact with humans but also from interactions with humans. In both cases understanding human behavior is crucial. Human state and intentions are very ambiguous and uncertain, fusing information from multiple sensing and input modalities might allow the robot to disambiguate. For example, in imitation learning approaches instructions from one modality typically are not complete. Here the challenge lies in learning which information, from which modality is relevant for the task, and which is not.

This vacancy is part of the project Open Deep Learning Toolkit for Robotics (OpenDR) https://opendr.eu/ . The aim of OpenDR is to develop a modular, open and non-proprietary deep learning toolkit for robotics. We will provide a set of software functions, packages and utilities to help roboticists develop and test robotic applications that incorporate deep learning. OpenDR will enable linking robotics applications to software libraries such as TensorFlow and the ROS operating environment. We focus on the AI and cognition core technology in order to give robotic systems the ability to interact with people and environments by means of deep-learning methods for active perception, cognition and decisions making. OpenDR will enlarge the range of robotics applications making use of deep learning, which will be demonstrated in the applications areas of healthcare, agri-food and agile production. The project is funded by the EU Horizon 2020 program, call H2020-ICT-2018-2020 (Information and Communication Technologies), 2019 – 2023.

The main focus of the Cognitive Robotics department is the development of intelligent robots and vehicles that will advance mobility, productivity and quality of life. Our mission is to bring robotic solutions to human-inhabited environments, focusing on research in the areas of machine perception, motion planning and control, machine learning, automatic control and physical interaction of intelligent machines with humans. We combine fundamental research with work on physical demonstrators in areas such as self-driving vehicles, collaborative industrial robots, mobile manipulators and haptic interfaces. Strong collaborations exist with cross-faculty institutes TU Delft Robotics Institute and TU Delft Transport Institute), our national robotic ecosystem (RoboValley, Holland Robotics) and international industry and academia. http://www.cor.tudelft.nl/