The validation of systems based on deep learning for use in safety-critical applications proves to be inherently difficult, since their subsymbolic mode of operation does not provide adequate levels of abstraction for representation and proof of correctness. The VeryHuman project aims to synthesize such levels of abstraction by observing and analysing the behaviour of upright walking of a two-legged humanoid robot. The theory to be developed is the starting point for the definition of an appropriate reward function to optimally control the movements of the humanoid by means of enhanced learning, as well as for verifiable abstraction of the corresponding kinematic models, which can be used to validate the behaviour of the robot more easily.
Partners
Cyber Physical Systems (CPS), DFKI Robotics Innovation Center (RIC), DFKI
