Biblio
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2018. Skill Memories for Parameterized Dynamic Action Primitives on the Pneumatically Driven Humanoid Robot Child Affetto. ICDL-Epirob 2018.
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2016. Incremental Bootstrapping of Parameterized Motor Skills. Proc. IEEE Humanoids.
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2014. An Active Compliant Control Mode for Interaction with a Pneumatic Soft Robot. 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014). :573–579.
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2011. Automatic control of ball and beam system using Particle Swarm Optimization. IEEE 12th International Symposium on Computational Intelligence and Informatics.
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2019. Online Associative Multi-Stage Goal Babbling Toward Versatile Learning of Sensorimotor Skills. Int. Conference Developmental Learning. :327-334.
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2017. Learning Inverse Statics Models Efficiently. arXiv.
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2018. Learning Inverse Statics Models Efficiently with Symmetry-Based Exploration. Frontiers in Neurorobotics.
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2016. Goal Babbling with direction sampling for simultaneous exploration and learning of inverse kinematics of a humanoid robot. Proceedings of the workshop on New Challenges in Neural Computation. 4:56–63.
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2018. Multi-Stage Goal Babbling for Learning Inverse Models Simultaneously.. IROS workshop.
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2019. An architecture for AutomationML-based constraint modelling and orchestration of Incremental Manufacturing. 7th CIRP Global Web Conference.
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2011. Reservoir regularization stabilizes learning of Echo State Networks with output feedback. Proc. European Symposium on Artificial Neural Networks. :59–64.
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2017. Hybrid Analytical and Data-driven Modeling for Feed-forward Robot Control. Sensors. 17(2)
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2014. Efficient Policy Search with a Parameterized Skill Memory. :1400–1407.
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2011. State prediction: a constructive method to program recurrent neural networks. Artificial Neural Networks and Machine Learning – ICANN 2011 : 21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part I. 6791:159–166.
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2009. Reaching movement generation with a recurrent neural network based on learning inverse kinematics for the humanoid robot iCub. IEEE Conf. Humanoid Robotics. :323–330.
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2018. Maschinelles Lernen in technischen Systemen. Steigerung der Intelligenz mechatronischer Systeme. :pp.73-118.
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2010. Tacking reduces bow-diving of high-speed unmanned sea surface vehicles. Int. Symp. Learning and Adaptive Behavior in Robotic Systems. :177–182.
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2012. Representation and Generalization of Bi-manual Skills from Kinesthetic Teaching. IEEE-RAS International Conference on Humanoid Robots. :560–567.
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2016. Hybrid Mechanical and Data-driven Modeling Improves Inverse Kinematic Control of a Soft Robot. Procedia Technology. 26:12–19.
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2009. Attractor-based computation with reservoirs for online learning of inverse kinematics. European Symposium Artificial Neural Networks (ESANN). :257–262.
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2011. A constrained regularization approach for input-driven recurrent neural networks. Differential Equations and Dynamical Systems. 19:27–46.
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2012. Regularization and stability in reservoir networks with output feedback. Neurocomputing. 90:96–105.
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2009. Goal-directed movement generation with a transient-based recurrent neural network controller. Advanced Technologies for Enhanced Quality of Life. :112–117.
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2011. Neural learning and dynamical selection of redundant solutions for inverse kinematic control. Proc. IEEE Int. Conf. Humanoid Robots. :564–569.
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2015. Efficient Policy Search in Low-dimensional Embedding Spaces by Generalizing Motion Primitives with a Parameterized Skill Memory. Autonomous Robots. 38:331–348.
