M-Rock

Human-Machine Interaction Modeling for Continuous Improvement of Robot Behavior

Embedding implicit and explicit feedback into the Q-Rock Development Cycle (Photo: Thomas Röhr, DFKI GmbH)
Embedding implicit and explicit feedback into the Q-Rock Development Cycle (Photo: Thomas Röhr, DFKI GmbH)

M-Rock is part of the X-Rock developments, enabling users to design personal assistants without any expert knowledge and help domain experts to identify opportunities to improve a system. M-Rock builds on the results of D-Rock and Q-Rock: Modularization and modelling solutions developed in D-Rock enable efficient reuse of components and describe 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. The main goal of M-Rock is to enable the use of user feedback to not only improve behaviours on the software side with respect to the individual requirements of the user, as shown in the Q-Rock outlook, but also to realize a subsequent optimization of the Q-Rock software flow with respect to the hardware selection. M-Rock thus enables an automatic adaptation of a robot to the individual requirements and preferences of the interacting human. For this purpose, explicit feedback (e.g., a rating scale for performance evaluation) is combined with implicit feedback. As a source of implicit human feedback, M-Rock makes use of the EEGs of the users. Using two different rating scenarios, we evaluate the developments in M-Rock to validate that both laymen and domain experts can use them.

Duration: 01.08.2021 till 31.07.2024
Donee: German Research Center for Artificial Intelligence GmbH
Sponsor: Federal Ministry of Education and Research
Grant number: Funded by the Federal Ministry of Education and Research with grant no01IW21002.
Application Field: Assistance- and Rehabilitation Systems
Logistics, Production and Consumer
SAR- & Security Robotics
Underwater Robotics
Space Robotics
Related Projects: D-Rock
Models, methods and tools for the model based software development of robots (06.2015- 05.2018)
Q-Rock
AI-based Qualification of Deliberative Behaviour for a Robotic Construction Kit (08.2018- 07.2021)
Recupera REHA
Full-body exoskeleton for upper body robotic assistance (09.2014- 12.2017)
Related Robots: RH5
Humanoid robot as an assistance system in a human-optimized environment
RH5 Manus
Humanoid robot as an assistance system in a human-optimized environment
Dual Arm Exoskeleton
Exoskeleton for upper body robotic assistance (Recupera REHA)
Related Software: HyRoDyn
Hybrid Robot Dynamics
Rock
Robot Construction Kit
BOLeRo
Behavior Optimization and Learning for Robots
Phobos
An add-on for Blender allowing editing and exporting of robots for the MARS simulation
MARS
Machina Arte Robotum Simulans
pySPACE
Signal Processing and Classification Environment written in Python

Project details

Graphical User Interface for rating of robot behaviors (Photo: Thomas Röhr, DFKI GmbH)
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.
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last updated 21.12.2021
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