Biblio
.
2016. Modulare Fertigungslinien für die individualisierte Produktion. Werkstattstechnik online. 4:204-210.
.
2017. Modelling of Parametrized Processes via Regression in the Model Space of Neural Networks. Neurocomputing. 268(C):55-63.
.
2016. Modelling of Parameterized Processes via Regression in the Model Space. Proceedings of 24th European Symposium on Artificial Neural Networks. :53–58.
.
2025. Modeling of Deformable Linear Objects Under Incomplete State Information. Int. Conf. Robotics and Automation (ICRA).
.
2018. Modeling & Control of Multi-Arm and Multi-Leg Robots: Compensating for Object Dynamics during Grasping. Int. Conf. Robotics and Automation.
.
2014. Model-free Path Planning for Redundant Robots using Sparse Data from Kinesthetic Teaching. Proc. of the Int. Conference on Intelligent Robots and Systems (IROS). :4381–4388.
.
2010. Mit Kopf, Körper und Hand: Herausforderungen Humanoider Roboter. Automatisierungstechnik, special issue on "humnoid robotics". 58:630–638.
.
2023. Mit KI zu mehr Teilhabe in der Arbeitswelt: Potenziale, Einsatzmöglichkeiten und Herausforderungen. Whitepaper aus der Plattform Lernende Systeme.
.
2005. Memory in Backpropagation-Decorrelation O(N) Efficient Online Recurrent Learning. LNCS. 3697:649–654.
.
1999. Maximisation of stability ranges for recurrent neural networks subject to on-line adaptation. Proc. European Symposium Artificial Neural Networks. :369–374.
.
2010. Mastering Growth while Bootstrapping Sensorimotor Coordination. Int. Conf. on Epigenetic Robotics.
.
2019. Maschinelles Lernen und lernende Assistenzsysteme - Neue Tätigkeiten, Rollen und Anforderungen für Beschäftigte? Berufsbildung in Wissenschaft und Praxis – BWP. 3:14-18.
.
2018. Maschinelles Lernen in technischen Systemen. Steigerung der Intelligenz mechatronischer Systeme. :pp.73-118.
.
2011. Manual Intelligence as a Rosetta Stone for Robot Cognition. Robotics Research. 66:135–146.
.
2002. Local structural stability of recurrent networks with time-varying weights. Neurocomputing. 48:39–51.
.
2000. Local input-output stability of recurrent networks with time-varying weights. Proc. European Symposium Artificial Neural Networks. :281–286.
.
2020. Let's Work Together: A Meta-Analysis on Robot Design Features that Enable Successful Human–Robot Interaction at Work. Human Factors.
.
2024. Lessons Learned from Investigating Robotics-Based, Human-like Testing of an Upper-Body Exoskeleton . Applied Sciences. 14(6)
.
2013. Lernen und Sicherheit in Interaktion mit Robotern aus Maschinensicht. Robotik und Gesetzgebung. 2:51–71.
.
2012. Learning Whole Upper Body Control with Dynamic Redundancy Resolution in Coupled Associative Radial Basis Function Networks. IROS. :1487–1492.
.
2012. Learning visuo-motor coordination for pointing without depth calculation. Proc. European Symposium on Artificial Neural Networks. :91–96.
.
2013. Learning the rules of a game: neural conditioning in human-robot interaction with delayed rewards.
.
2015. Learning Robot Motions with Stable Dynamical Systems under Diffeomorphic Transformations. Robotics and Autonomous Systems. 70:1–15.

] 

