Project description
An improved robot learning methodology
With robotics being extremely useful for a number of sectors and for their automation, much effort is being directed towards the development and programming of robots. Currently, the methods by which robots are programmed and learn are imitation learning (robot learns by mimicking the teacher) and reinforcement learning (robot learns through trial and error). Unfortunately, both of these methods are time-consuming and expensive, even for mildly difficult tasks. The EU-funded TERI project aims to revolutionise robot learning by introducing a new methodology, intermittent robot-teacher interaction, a method that is used efficiently by humans. This will allow the teacher to better guide and correct the robot during its learning process, thus providing better results, faster and cheaper.
Objective
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 realize 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 infeasibly many 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 optimize the task performance. It will enable the user to directly perceive and correct undesirable behavior and to quickly guide the robot toward the target behavior. In my previous research I have made ground-breaking contributions to the existing learning paradigms and I am therefore ideally prepared to tackle the three-fold 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.
Fields of science
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Funding Scheme
ERC-STG - Starting GrantHost institution
2628 CN Delft
Netherlands