24 Aug - 2018

Robotics researcher Jens Kober has received an ERC Starting Grant from the European Research Council. Kober works as assistant professor at the Cognitive Robotics Department from Delft University of Technology and is associated to TU Delft Robotics Institute and RoboValley.

Kober received the prestigious grant for his project Teaching Robots Interactively. Programming and re-programming robots is extremely time-consuming and expensive, which presents a major bottleneck for new industrial, agricultural, care, and household robot applications. Kober’s goal is to realise a scientific breakthrough in enabling robots to learn how to perform manipulation tasks from few human demonstrations, based on novel interactive machine learning techniques (see below for a summary).

“This project will deliver a completely new and better approach”, he states. “Robot learning will no longer rely on initial demonstrations only, but it will effectively use additional user feedback to continuously optimise task performance.”

"This project will deliver a completely new and better approach to robot learning"

The laureate is assistant professor in Delft since 2015, where he worked at the Delft Center for Systems and Control, before joining the Cognitive Robotics Department. He was awarded the IEEE-RAS Early Academic Career Award in Robotics and Automation 2018. He completed his PhD in 2012 jointly at the Technische Universität Darmstadt and the Max-Planck Institute for Intelligent Systems. His Ph.D. thesis won the 2013 Georges Giralt PhD Award for the best Robotics PhD thesis in Europe in 2012.

The ERC Starting Grants are awarded to the best and most creative scientists from all conceivable research areas throughout Europe. They mainly target researchers with two to seven years of experience in a postdoctorate. The grant is worth 1.5 million euros for a five-year programme.

Jens Kober about the project Teaching Robots Interactively:

"Programming and re-programming robots is extremely time-consuming and expensive, which presents a major bottleneck for new industrial, agricultural, care, and household robot applications. My goal is to realise a scientific breakthrough in enabling robots to learn how to perform manipulation tasks from few human demonstrations, based on novel interactive machine learning techniques.

Current robot learning approaches focus either on imitation learning (mimicking the teacher’s movement) or on reinforcement learning (self-improvement by trial and error). Learning even moderately complex tasks in this way still requires an infeasible number of iterations or task-specific prior knowledge that needs to be programmed in the robot. To render robot learning fast, effective and efficient, I propose to incorporate intermittent robot-teacher interaction, which so far has been largely ignored in robot learning although it is a prominent feature in human learning.

This project will deliver a completely new and better approach: robot learning will no longer rely on initial demonstrations only, but it will effectively use additional user feedback to continuously optimise task performance. It will enable the user to directly perceive and correct undesirable behaviour and to quickly guide the robot toward the target behaviour.

In my previous research I have made ground-breaking contributions to the existing learning paradigms and I am therefore ideally prepared to tackle the threefold challenge of this project: developing theoretically sound techniques which are at the same time intuitive for the user and efficient for real-world applications.

The novel framework will be validated with generic real-world robotic force-interaction tasks related to handling and (dis)assembly. The potential of the newly developed teaching framework will be demonstrated with challenging bi-manual tasks and a final study evaluating how well novice human operators can teach novel tasks to a robot."


Faculties

3mE

This website uses cookies to enhance user experience and to provide us with visitor analytics.