Relevant for Research Area

B - Core Technologies

C - Applications


Brain-controlled assistive robots hold the promise of restoring autonomy to paralyzed patients. Existing approaches are based on low-level, continuous control of robotic devices, resulting in a high cognitive load for their users. In the NeuroBots project, in contrast, we enhance prosthetic devices with a certain degree of autonomy and adaptivity to enable control on a higher cognitive level. To achieve this, we develop new methods and technologies in core areas of brain-machine interfaces, as well as artificial intelligence. This includes innovative approaches to brain-signal decoding with deep neural networks, efficient motion planning and improved perception for mobile robots and manipulators, novel methods for deep reinforcement learning, hierarchical planning with user feedback, and evaluation of formal methods for safety guarantees. The different components are continually integrated in an architecture based on the Robot Operating System (ROS), realizing a demonstrator of the BrainLinks-BrainTools LiNC concept.