NeuroBots

BRAIN-CONTROLLED INTELLIGENT ROBOTIC DEVICES


Relevant for Research Area

B - Core Technologies

C - Applications


Summary

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.


Research Status

The most outstanding result of the project is a fully integrated system that realizes the BrainLinks-BrainTools LiNC concept, combining state-of-the-art online decoding of neuronal control signals with deep neural networks, high-level hierarchical planning with graphical user interface based on the planner’s world knowledge, as well as novel perception and low-level robot planning algorithms for mobile robots. Improved high-level brain-signal decoding and closed-loop human-robot interaction are in the focus of current research.

Schröer S, Killmann I, Frank B, Völker M, Fiederer LD, Ball T, Burgard W (2015) An Autonomous Robotic Assistant for Drinking. Proceedings of 2015 IEEE International Conference on Robotics and Automation.

Göbelbecker M (2015) Assisting with Goal Formulation for Domain Independent Planning. Advances in Artificial Intelligence, volume: 9324 of the series Lecture Notes in Computer Science, pp. 87-99.

Burget F, Bennewitz M, Burgard W (2016) BI2RRT*: An Efficient Sampling-Based Path Planning Framework for Task-Constrained Mobile Manipulation. Proceedings of the IEEE/RSJ Int. Conf. On Intelligent Robots and Systems (IROS) 2016, pp. 3714-21.

Do C, Schubert T, Burgard W (2016) A Probabilistic Approach to Liquid Level Detection in Cups Using an RGB-D Camera. Proceedings of the IEEE/RSJ Int. Conf. On Intelligent Robots and Systems (IROS) 2016 (student best-paper award runner up), pp. 2075-80

F. Burget, L.D.J. Fiederer, D. Kuhner, M. Völker, J. Aldinger, R.T. Schirrmeister, C. Do, J. Boedecker, B. Nebel, T. Ball, and W. Burgard (2017) Acting Thoughts: Towards a Mobile Robotic Service Assistant for Users with Limited Communication Skills. In European Conference on Mobile Robots (ECMR), 2017

P. Jund, A. Eitel, N. Abdo, and W. Burgard (2018) Optimization Beyond the Convolution: Generalizing Spatial Relations with End-to-End Metric Learning. In International Conference in Robotics and Automation (ICRA), 2018.

Neurobots Demonstrator: https://www.youtube.com/watch?v=Ccor_RNHUAA