Predictive robots: our future assistants – Interview with Prof Elsa Kirchner

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One of the greatest challenges in human-machine interfaces is natural interaction. Technology has already made great progress with solutions such as gesture and voice control. Recently, the focus has also turned to controlling machines by thought: brain-machine interfaces measure the brain’s EEG signals and derive control commands for computers, machines or robots from them. One of the pioneers in the use of EEG data for interaction with robotic systems is the German Research Center for Artificial Intelligence.
Here, the EXPECT research project is currently underway, whose main goal is to develop an adaptive, self-learning platform for human-robot collaboration. It should not only enable various types of active interaction, but also be able to deduce the human’s intention from gestures, language, eye movements and brain activity – the machine should therefore be able to guess what the human is going to do next.
An exciting project – which is why we are very pleased that we can welcome Prof. Dr. Elsa Kirchner today. She is EXPECT’s project manager for the Robotics Innovation Center research area and will be able to give us interesting insights into the project and the state of research on brain-machine interfaces.
This podcast interview is part of our magazine issue The Quintessence of Human Machine Interfaces that is available free of charge on you mobile devices. Download our App and enjoy insights into various technology fields.
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