M-Rock

Current digitalisation developments, AI-based data processing, and powerful hardware lay the groundwork for future embodied AI assistants. These intelligent robots must be versatile, adaptive and flexible to changes in the environment or requirements to become optimised for their purpose for assistance in everyday life and work. Furthermore, they must automatically adapt to perceived new requirements and the changing user's needs.

However, current systems typically do not allow the user to decide how a system should look and behave. This is true for personal assistance, cobots used in production, logistics, or care sectors. Thus, the X-ROCK project series addresses precisely these challenges. X-ROCK enables users to design their personal assistants without any expert knowledge, but it will also help domain experts identify possibilities for improving a system. Modularisation and modelling developed in D-ROCK enables efficient reuse of components and describes how components can be used in a given context. Q-ROCK automatically maps the structural hardware and software complexity of current robotic systems to behaviours.

M-ROCK will directly build on the results of D-ROCK and Q-ROCK. M-ROCK's primary goal is to enable the usage of explicit and implicit user feedback to not only optimise behaviour on the software side to accommodate the user's individual requirements, as shown in Q-ROCK's outlook but also to enable subsequent optimisation of the Q-ROCK software flow, including hardware selection. To this end, we combine explicit feedback (i.e., a rating scale for performance evaluation) with implicit feedback (users' EEG signals).

With the help of two different evaluation scenarios, we will show how the developments in M-ROCK can be used to optimise the software and hardware of a robot by enabling the usage of explicit and implicit human feedback within the Q-ROCK cycle and how it can be used by laymen as well as by domain experts alike.