Publikationen

• 2023
• 2022
• 2021
• 2020
• 2019
• 2018
• 2017
• 2016
• 2015
• 2014
• 2013
• 2012
• 2011
• 2010
• 2009
• 2008
• 2007
• 2006
• 2005
• 2003

2023

• Schilling, M., & Cruse, H. (2023). neuroWalknet, a controller for hexapod walking allowing for context dependent behavior. PLoS Computational Biology, 19(1), Article e1010136. doi: 10.1371/journal.pcbi.1010136.
• Schilling, M.; Hammer, B.; Ohl, F.W.; Ritter, H., & Wiskott, L. (2023). Modularity in Nervous Systems—a Key to Efficient Adaptivity for Deep Reinforcement Learning. Cognitive Computation, in press. doi: 10.1007/s12559-022-10080-w.
• Simmering, J; Hermes, L; Schneider, A, & Schilling, M. (2023). Adaptation of a Decentralized Controller to Curve Walking in a Hexapod Robot. In Cascalho JM; Tokhi MO; Silva MF; Mendes A; Goher K; Funk M (Ed.): Robotics in Natural Settings , pp. 264–275. Cham: Springer International Publishing.
• El Amri, W.Z.; Hermes, L., & Schilling, M. (2023). Hierarchical Decentralized Deep Reinforcement Learning Architecture for a Simulated Four-Legged Agent. In G. Nicosia et al. (Ed.): Proc. of 8th International Online & Onsite Conference on Machine Learning, Optimization, and Data Science , pp. 1–16. Switzerland: Springer Nature. doi: https://doi.org/10.1007/978-3-031-25891-6_20.
• Hermes, Luca; Liuliakov, Aleksei; Schilling, Malte. (2023). Graph Learning by Dynamic Sampling. In Verma, Brijesh; Kasabov, Nicola K. (Eds.): IJCNN 2023 Conference Proceedings , pp. 1–8. Gold Coast: Wiley-IEEE Computer Society Press. doi: 10.1109/IJCNN54540.2023.10191846.
• Mindlin, Dimitry; Schilling, Malte; Cimiano, Philipp. (2023). ABC-GAN: Spatially Constrained Counterfactual Generation for Image Classification Explanations. In Longo, Luca (Eds.): Explainable Artificial Intelligence , pp. 260–282. Lissabon: Springer. doi: 10.1007/978-3-031-44064-9_15.

2022

• Schilling, Malte; Paskarbeit, J.; Ritter, H.; Schneider, A., & Cruse, H. (2022). From Adaptive Locomotion to Predictive Action Selection – Cognitive Control for a Six-Legged Walker. IEEE Transactions on Robotics, 38(2), 666–682. doi: 10.1109/TRO.2021.3106832.
• Schilling, M. (2022). Coding Challenge. In Gerick, J.; A. Sommer, A.; Zimmermann, G. (Eds.), Kompetent Prüfungen gestalten – 60 Prüfungsformate für die Hochschullehre (pp. 108–111). Münster: Waxmann.
• Hermes, L.; Hammer, B.; Melnik, A.; Velioglu, R.; Vieth, M., & Schilling, M. (2022). A Graph-based U-Net Model for Predicting Traffic in unseen Cities. In Gori, M.; Sperduti, A. (Eds.): Proceedings of the International Joint Conf. on Neural Networks , pp. 1–8. Bari: Wiley-IEEE Press.
• Eichenberger, C.; Neun, M.; Martin, H.; Herruzo, P.; Spanring, M.; Lu, Y.; Choi, S.; Konyakhin, V.; Lukashina, N.; Shpilman, A.; Wiedemann, N.; Raubal, M.; Wang, B.; Vu, H.L.; Mohajerpoor, R.; Cai, C.; Kim, I.; Hermes, L.; Melnik, A.; Velioglu, R.; Vieth, M.; Schilling, M.; Bojesomo, A.; Al-Marzouqi, H.; Liatsis, P.; Santokhi, J.; Hillier, D.; Yang, Y.; Sarwar, J.; Jordan, A.; Hewage, E.; Jonietz, D.; Tang, F.; Gruca, A.; Kopp, M.; Kreil, D.P., & Hochreiter, S. (2022). Traffic4cast at NeurIPS 2021 - Temporal Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes. In Ranzato, M; Beygelzimer, A; Dauphin, Y; Liang, PS; Wortman Vaughan, J (Eds.): Proceedings of the NeurIPS 2021 Competitions Demonstrations Track, PMLR , pp. 97–112. Red Hook: Curran Associates.

2021

• Hermes, L.; Hammer, B., & Schilling, M. (2021). Application of Graph Convolutions in a Lightweight Model for Skeletal Human Motion Forecasting. In Verleysen, M. (Eds.): Proceedings of 29th European Symposium on Artificial Neural Networks , pp. 111–116. Bruges: i6doc.
• Schilling, Malte; Melnik, Andrew; Ohl, Frank W.; Ritter, Helge; Hammer, Barbara. (2021). Decentralized control and local information for robust and adaptive decentralized Deep Reinforcement Learning. Neural Networks, 144, 699–725. doi: 10.1016/j.neunet.2021.09.017.
• Artelt, A; Vaquet, V; Velioglu, R; Hinder, F; Brinkrolf, J; Schilling, M, & Hammer, B. (2021). Evaluating Robustness of Counterfactual Explanations. In Crockett, K.; Mostaghim, S. (Eds.): 2021 IEEE Symposium Series on Computational Intelligence (SSCI) , pp. 01–09. Orlando, Florida: Wiley-IEEE Press. doi: 10.1109/SSCI50451.2021.9660058.
• Schilling, M. (2021). Avoid Overfitting in Deep Reinforcement Learning: Increasing Robustness Through Decentralized Control. In Farkas, I; Masulli, P; Otte, S; Wermter, S (Eds.): Artificial Neural Networks and Machine Learning -- ICANN 2021. 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14--17, 2021, Proceedings, Part IV , pp. 638–649. Bratislava: Springer International Publishing. doi: 10.1007/978-3-030-86380-7_52.
• Lach, L; Korthals, T; Ferro, F; Ritter, H, & Schilling, M. (2021). Guiding Representation Learning in Deep Generative Models with Policy Gradients. In Dorronsoro B; Amodeo L; Pavone M; Ruiz P (Ed.): Optimization and Learning. 4th International Conference, OLA 2021, Catania, Italy, June 21-23, 2021, Proceedings , pp. 115–131. Catania: Springer International Publishing. doi: 10.1007/978-3-030-85672-4_9.
• Schilling, M.; Burgahn, C., & Fortmann, R. (2021). Evaluation of Graph Convolutions for Spatio-Temporal Predictions of EV-Charge Availability. In / (Ed.): Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI 2021) , pp. 1–6. Orlando: Wiley-IEEE Press. doi: 10.1109/SSCI50451.2021.9660162.

2020

• Mohanty, S.; Czakon, J.; Kaczmarek, K.A.; Pyskir, A.; Tarasiewicz, P.; Kunwar, S.; Rohrbach, J.; Luo, D.; Prasad, M.; Fleer, S.; Göpfert, J.P.; Tandon, A.; Mollard, G.; Rayaprolu, N.; Salathé, M., & Schilling, M. (2020). Deep Learning for Understanding Satellite Imagery: An Experimental Survey. Frontiers in artificial intelligence, 3, 534696–534696. doi: 10.3389/frai.2020.534696.
• Schilling, Malte Rohlfing, K.J.; Vogt, P.; Yu, C., & Spranger, M. (2020). Guest Editorial Special Issue on Multidisciplinary Perspectives on Mechanisms of Language Learning. IEEE Transactions on Cognitive and Developmental Systems, 12(2), 134–138. doi: 10.1109/tcds.2020.2991470.
• Schilling, M., & Cruse, H. (2020). Decentralized control of insect walking: A simple neural network explains a wide range of behavioral and neurophysiological results. PLoS Computational Biology, 16(4), e1007804–e1007804. doi: 10.1371/journal.pcbi.1007804.
• Schilling, Malte; Chang, N.; Rohlfing, K.J., & Spranger, M. (2020). Simulation across representation: The interplay of schemas and simulation-based inference on different levels of abstraction. Behavioral and Brain Sciences, 43, e147. doi: 10.1017/S0140525X19003169.
• Schilling, M; Konen, K, & Korthals, T. (2020). Modular Deep Reinforcement Learning for Emergent Locomotion on a Six-Legged Robot. In Agrawal, S. (Eds.): Proceedings of the 8th IEEE RAS/EMBS International Conference for Biomedical Robotics Biomechatronics (BioRob) , pp. 946–953. New York: Wiley-IEEE Press. doi: 10.1109/BioRob49111.2020.9224332.
• Bach, N; Melnik, A; Schilling, M; Korthals, T, & Ritter, H. (2020). Learn to Move Through a Combination of Policy Gradient Algorithms: DDPG, D4PG, and TD3. In Nicosia, G; et al. (Eds.): Lecture Notes in Computer Science , pp. 617–630. Cham: Springer.
• Schilling, M.; Konen, K.; Ohl, F.W., & Korthals, T. (2020). Decentralized Deep Reinforcement Learning for a Distributed Adaptive Locomotion Controller of a Hexapod Robot. In / (Ed.): Proceedings of IEEE/RSJ International Conference on Intelligent Robots Systems , pp. 5335–5342. Las Vegas: Wiley-IEEE Press.

2019

• Schilling, M.; Burgard. W.; Muelling, K.; Wrede, B., & Ritter, H. (2019). Editorial: Shared Autonomy- Learning of Joint Action and Human-Robot Collaboration. Frontiers in Neurorobotics, 13, 16–16. doi: 10.3389/fnbot.2019.00016.
• Dürr, V.; Arena, P.; Cruse, H.; Dallmann, C.J.; Hoinville, T.; Krause, T.; Drimus, A.; Matefi-Tempfli, S.; Paskarbeit, J.; Patanè, L.; Schaeffersmann, M.; Schilling, M.; Schmitz, J.; Strauss, R.; Theunissen, L.M.; Vitanza, A., & Schneider, A. (2019). Integrative Biomimetics of Autonomous Hexapedal Locomotion. Frontiers in Neurorobotics, 13, 88–88. doi: 10.3389/fnbot.2019.00088.
• Korthals, T; Schilling, M, & Leitner, J. A Perceived Environment Design using a Multi-Modal Variational Autoencoder for learning Active-Sensing. arXiv (2019). doi: 10.48550/arXiv.1911.00584.
• Melnik, A; Fleer, S; Schilling, M, & Ritter, H. Modularization of End-to-End Learning: Case Study in Arcade Games. arxiv (2019).
• Schilling, M.; Ritter, H.; Ohl, F.W. (2019). From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning — Insights from Biological Systems on Adaptive Flexibility. In / (Ed.): Proceedings of the 2019 IEEE International Conference on Systems, Man, Cybernetics , pp. 1472–1478. Bari: Wiley-IEEE Press. doi: 10.1109/SMC.2019.8914302.
• Schilling, Malte. (2019). Setup of a Recurrent Neural Network as a Body Model for Solving Inverse Forward Kinematics as well as Dynamics for a Redundant Manipulator. In / (Ed.): Proc. of the International Joint Conference on Neural Networks 2019 , pp. 1–8. Budapest: Wiley-IEEE Press.
• Schilling, M. (2019). Hierarchical Dual Quaternion-Based Recurrent Neural Network as a Flexible Internal Body Model. In / (Ed.): Proc. of the International Joint Conference on Neural Networks 2019 , pp. 1–8. Budapest: Wiley-IEEE Press.

2018

• Dürr, V., & Schilling, M. (2018). Transfer of Spatial Contact Information Among Limbs and the Notion of Peripersonal Space in Insects. Frontiers in Computational Neuroscience, 12, 101–101. doi: 10.3389/fncom.2018.00101.
• Schilling, M., & Melnik, A. (2018). An Approach to Hierarchical Deep Reinforcement Learning for a Decentralized Walking Control Architecture. In Samsonovich, A. (Eds.): Advances in Intel. Systems Computing , pp. 272–282. Cham: Springer.
• Kidziński Ł; Mohanty SP; Ong C; Huang Z; Zhou S; Pechenko A; Stelmaszczyk A; Jarosik P; Pavlov M; Kolesnikov S; Plis S; Chen Z; Zhang Z; Chen J; Shi J; Zheng Z; Yuan C; Lin Z; Michalewski H; Miłoś P; Osiński B; Melnik A; Schilling M; Ritter H; Carroll S; Hicks J; Levine S; Salathé M, & Delp S. (2018). Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments. In Escalera, S.; Weimer, M. (Eds.): The NIPS 2017 Competition: Building Intelligent Systems , pp. 121–154. Long Beach: Springer.
• Schilling, M. (2018). Coding Challenge. In Gerick J; Sommer A; Zimmermann G (Ed.), Kompetent Prüfungen gestalten -- 53 Prüfungsformate für die Hochschullehre (pp. 42–45). Münster: Waxmann.
• Schilling, M, & Cruse, H. (2018). Getting cognitive. In Bläsing B; Puttke M; Schack T (Ed.), The Neurocognition of Dance. Mind, movement and motor skills (pp. 150–168). New York: Routledge.
• Cruse, H, & Schilling, M. (2018). Pattern Generation. In Prescott, TJ; Lepora, N; Verschure, PF (Eds.), Living Machines. A handbook of research in biomimetics and biohybrid systems (pp. 218–226). Oxford: Oxford University Press. doi: 10.1093/oso/9780199674923.003.0024.

2017

• Schilling, M., & Cruse, H. (2017). ReaCog, a Minimal Cognitive Controller Based on Recruitment of Reactive Systems. Frontiers in Neurorobotics, 11, Article 3. doi: 10.3389/fnbot.2017.00003.
• Schilling, M. (2017). Old Actions in Novel Contexts - a Cognitive Architecture for Safe Explorative Action Selection. In Bryson, J. et al. (Eds.): Proceedings of the Artificial Intelligence and Simulation of Behaviour Conference (AISB 2017) , pp. 286–292. Red Hook, NY: Curran Associates.

2016

• Schilling, M., & Cruse, H. (2016). Avoid the hard problem: Employment of mental simulation for prediction is already a crucial step. Proceedings of the National Academy of Sciences of the United States of America (PNAS), 113(27), E3811–E3811. doi: 10.1073/pnas.1607146113.
• Paskarbeit, J.; Otto, M.; Schilling, M., & Schneider, A. (2016). Stick(y) Insects — Evaluation of Static Stability for Bio-inspired Leg Coordination in Robotics. In Lepora, N.F.; Mura, A.; Mangan, M.; Verschure, P.; Desmulliez, M.; Prescott, T.J. (Eds.): Proceedings of the 5th International Conference on Living Machines , pp. 239–250. Cham: Springer International Publishing. doi: 10.1007/978-3-319-42417-0_22.
• Schilling, M; Kopp, S; Wachsmuth, S; Wrede, B; Ritter, H; Brox, T; Nebel, B, & Burgard, W. (2016). Towards A Multidimensional Perspective on Shared Autonomy. In / (Ed.): Proceedings of the AAAI Fall Symposium Series 2016, Stanford (USA) , pp. 338–344. Stanford: AAAI Press.
• Cruse, H, & Schilling, M. (2016). Mental states as emergent properties. From walking to consciousness. In Metzinger, T; Windt, JM (Eds.), Open Mind, Philosophy and the Mind Sciences in the 21st Century. Vol 1 (pp. 349–386). Cambridge: MIT Press.
• Lücking, P; Rohlfing, K; Wrede, B, & Schilling, M. (2016). Preschoolers' engagement in social interaction with an autonomous robotic system. In / (Ed.): 2016 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) , pp. 210–216. Paris: Wiley-IEEE Press. doi: 10.1109/devlrn.2016.7846821.
• Nomikou, I.; Schilling, M.; Heller, V. Rohlfing, K. J. (2016). Language at all times. Action interaction as contexts for enriching representations. Interaction Studies, 17(1), 128–153.
• Schilling, M. (2016). Lose a leg but not your head – a cognitive extension of a biologically-inspired walking architecture. Procedia Computer Science, 88, 102–106.

2015

• Paskarbeit, J; Schilling, M; Schmitz, J, & Schneider, A. (2015). Obstacle crossing of a real, compliant robot based on local evasion movements and averaging of stance heights using singular value decomposition. In / (Ed.): Proceedings IEEE International Conference on Robotics and Automation (ICRA) , pp. 3140–3145. Seattle: Wiley-IEEE Press. doi: 10.1109/ICRA.2015.7139631.
• Hoinville, T; Schilling, M, & Cruse, H. (2015). Control of rhythmic behavior: Central and Peripheral Influences to pattern Generation. In / (Ed.): ICRA 2015 CPG Workshop : CPGs for Locomotion Control: Pros, Cons & Alternatives , pp. 1–3. Seattle: Wiley-IEEE Press.
• Baum, M; Meier, M, & Schilling, M. (2015). Population based Mean of Multiple Computations networks: A building block for kinematic models. In / (Ed.): 2015 International Joint Conference on Neural Networks (IJCNN) , pp. 3616–3623. Killarney: Wiley-IEEE Press. doi: 10.1109/ijcnn.2015.7280791.
• Priesters, M; Schilling, M, & Kopp, S. (2015). Towards a layered framework for embodied language processing in situated human-robot interaction. In Howes, C; Larsson, S (Eds.): Proceedings of the 19th Workshop on the Semantics and Pragmatics of Dialogue , pp. 206–207. Gothenburg: University of Gothenburg.
• Cruse, H, & Schilling, M. The Bottom-Up Approach: Benefits and Limits. Open MIND (2015).: MIND Group. doi: 10.15502/9783958570931.

2014

• Hoinville, T; Krause, AF; Schilling, M, & Cruse, H. (2014). Bridging an Interspecies Gap? Toward Human-Insectoid Robot Interaction. In / (Ed.): Proceedings of 9th ACM/IEEE International Conference on Human-Robot Interaction (HRI 2014).
• Schneide, r A; Paskarbeit, J; Schilling, M, & Schmitz, J. (2014). HECTOR, a bio-inspired and compliant hexapod robot. In Duff, A; Prescott, T.; Verschure, P.; Lepora, N. (Eds.): Proceedings of the 3rd Conference on Biomimetics and Biohybrid Systems , pp. 427–430. Milan: Springer.
• Cruse, H, & Schilling, M. (2014). Action Selection within short time windows. In Duff A; Prescott T; Verschure P; Lepora N (Ed.): Proceedings of Biomimetic and Biohybrid Systems: Third International Conference, Living Machines 2014, Milan, Italy , pp. 47–58. Milan: Springer International Publishing. doi: 10.1007/978-3-319-09435-9_5.
• Schack, T; Bläsing, B; Hughes, C; Flash, T, & Schilling, M. (2014). Elements and Construction of Motor Control. In Papaioannou, A; Hackfort, D (Eds.), Routledge Companion to Sport and Exercise Psychology:Global Perspectives and Fundamental Concepts (pp. 306–321). London: Routledge. doi: 10.4324/9781315880198.ch20.

2013

• Schilling, M.; Paskarbeit, J.; Hoinville, T.; Hüffmeier, A.; Schneider, A.; Schmitz, J., & Cruse, H. (2013). A hexapod walker using a heterarchical architecture for action selection. Frontiers in Computational Neuroscience, 7(126), 126. doi: 10.3389/fncom.2013.00126.
• Cruse, H., & Schilling, M. (2013). How and to what end may consciousness contribute to action? Attributing properties of consciousness to an embodied, minimally cognitive artificial neural network. Frontiers in Psychology, 4(324), 324. doi: 10.3389/fpsyg.2013.00324.
• Schilling, M.; Hoinville, T.; Schmitz, J., & Cruse, H. (2013). Walknet, a bio-inspired controller for hexapod walking. Biological Cybernetics, 107(4), 397–419. doi: 10.1007/s00422-013-0563-5.
• Schilling, M, & Narayanan, S. (2013). Communicating with Executable Action Representations. In / (Ed.): Proceedings of AAAI Spring Symposium Series 2013, Stanford , pp. 1–8. Stanford: AAAI Press.

2012

• Schilling, M.; Rohlfing, K., & Cruse, H. (2012). Prediction as internal simulation: taking chances in what to do next. Frontiers in Psychology, 3(405), 405. doi: 10.3389/fpsyg.2012.00405.
• Schilling, M., & Cruse, H. (2012). What's Next: Recruitment of a Grounded Predictive Body Model for Planning a Robot's Actions. Frontiers in Psychology, 3, 383–383. doi: 10.3389/fpsyg.2012.00383.
• Schilling, Malte; Paskarbeit, J.; Schmitz, J.; Schneider, A., & Cruse, H. (2012). Grounded internal body models for communication: Integration of sensory, motor and visual spaces for mediating conceptualization. In Steels, L.; Hild, M. (Eds.), Language Grounding in Robots (pp. 131–150). Berlin: Springer.
• Schilling, M; Paskarbeit, J; Schmitz, J; Schneider, A, & Cruse, H. (2012). Grounding an internal body model of a hexapod walker - Control of curve walking in a biological inspired robot. In / (Ed.): Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems , pp. 2762–2768. Vilamoura: Wiley-IEEE Press. doi: 10.1109/iros.2012.6385709.
• Schilling, M; Paskarbeit, J; Schneider, A, & Cruse, H. (2012). Flexible internal body models for motor control: On the convergence of constrained dual quaternion mean of multiple computation networks. In / (Ed.): Proceedings of the International Joint Conference on Neural Networks 2012 , pp. 1–8. Brisbane: Wiley-IEEE Press. doi: 10.1109/ijcnn.2012.6252846.

2011

• Schilling, M. (2011). Universally manipulable body models --- dual quaternion representations in layered and dynamic MMCs. Autonomous Robots, 30(4), 399–425. doi: 10.1007/s10514-011-9226-3.
• Schilling, M. (2011). Learning by seeing---associative learning of visual features through mental simulation of observed action. In Dourest, R. et al. (Eds.): Proc. of the ECAL 2011, Paris , pp. 731–738. Paris: MIT Press.
• Schilling, M. (2011). Integrating multi-sensory input in the body model -- a RNN approach to connect proprioception, visual features and motor control. In / (Ed.): Proceedings of the International Joint Conference on Neural Networks 2011, San Jose (CA) , pp. 2836–2843. San Jose: Wiley-IEEE Press.
• Cruse, H, & Schilling, M. (2011). From egocentric systems to systems allowing for Theory of Mind and mutualism. In Lenaerts T; Giacobini M; Bersini H; Bourgine P; Dorigo M; Doursat R (Ed.): Advances in Artificial Life, ECAL 2011. Proceed. of the Eleventh Europ. Conf. on the Synthesis and Simulation of Living Systems , pp. 185–192. Paris: MIT Press.

2010

• Paskarbeit, J; Schmitz, J; Schilling, M, & Schneider, A. (2010). Layout and Construction of a Hexapod Robot with Increased Mobility. In / (Ed.): Proceedings of the 3rd IEEE RAS/EMBS International Conference on Biomedical Robotics Biomechatronics , pp. 621–625. Tokyo: Wiley-IEEE Press.
• Schilling, M. (2010). A universally manipulable body model: Application of the MMC principle to a Dual Quaternion Representation. In / (Ed.): Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2010 at WCCI 2010 IEEE World Congress on Computational Intelligence) , pp. 1–8. Barcelona: Wiley-IEEE Press.
• Cruse, H, & Schilling, M. (2010). Getting cognitive. In Bläsing, B; Puttke, M; Schack, T (Eds.), The Neurocognition of Dance (pp. 53–74). London: Psychology Press.
• Cruse, H, & Schilling, M. (2010). Learning and retrieval of hierarchically organized information in a simple, one-layered RNN. In Mochiyama, H; Tokhi, M; Fujimoto, H; Virk, G (Eds.): Proceedings of the International Joint Conference on Neural Networks (IJCNN 2010 at WCCI 2010 IEEE World Congress on Computational Intelligence) , pp. 173–180. New Jersey: World Scientific Publishing.
• Cruse, H, & Schilling, M. (2010). Chunking by naming: a nonlinear recurrent network for storing hierarchical memory contents. In / (Ed.): 18th IEEE Workshop on Nonlinear Dynamics of Electronic Systems : NDES 2010 , pp. 202–205. Dresden: Wiley-IEEE Press.
• Cruse, H., & Schilling, M. (2010). Learning retrieval of hierarchically organized information in a simple, one-layered RNN. In / (Ed.): Proc. of the International Joint Conf. on Neural Networks (IJCNN 2010 at WCCI 2010 IEEE World Congress on Computational Intelligence) , pp. 1–9. Barcelona: Wiley-IEEE Press.

2009

• Cruse, H.; Dürr, V.; Schilling, M., & Schmitz, J. (2009). Principles of insect locomotion. Cognitive Systems Monographs, 1, 43–96. doi: 10.1007/978-3-540-88464-4_2.
• Schilling, M. (2009). Dynamic equations in MMC networks: Construction of a dynamic body model. In / (Ed.): Proceedings of the 12th International Conference on Climbing and Walking Robots , pp. 1047–1054. London: World Scientific Publishing.
• Schneider, A; Schilling, M; Cruse, H, & Schmitz, J. (2009). Positive velocity feedback on a six-legged walking robot. In Tosun O; Akin H; Tokhi M; Virk G (Ed.): Proceedings of the 12th International Conference on Climbing and Walking Robots , pp. 623–630. London: World Scientific Publishing.
• Cruse, H; Dürr, V; Schilling, M, & Schmitz, J. (2009). A Bottom-up approach for cognitive control. In Arena P; Patané L (Ed.), Spatial temporal patterns for action-oriented perception in roving robots (pp. 179–218). Berlin: Springer. doi: 10.1007/978-3-540-88464-4_4.

2008

• Schmitz, J.; Schneider, A.; Schilling, M., & Cruse, H. (2008). No need for a body model: Positive velocity feedback for the control of an 18-DOF robot walker. Applied Bionics and Biomechanics, 5(3), 135–147.
• Schilling, M., & Cruse, H. (2008). The evolution of cognition -- from first order to second order embodiment. In Wachsmuth, I.; Knoblich, G. (Eds.), Modeling Communication with Robots and Virtual Humans (pp. 77–108). Berlin: Springer.
• Schilling, M; Schneider, A; Cruse, H, & Schmitz, J. (2008). Local control mechanisms in six-legged walking. In / (Ed.): Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems , pp. 2655–2660. Nice: Wiley-IEEE Press. doi: 10.1109/iros.2008.4650591.

2007

• Schilling, M.; Cruse, H., & Arena, P. (2007). Hexapod Walking: an expansion to Walknet dealing with leg amputations and force oscillations. Biological Cybernetics, 96(3), 323–340. doi: 10.1007/s00422-006-0117-1.
• Schilling, M, & Cruse, H. (2007). Hierarchical MMC networks as a manipulable body model. In / (Ed.): Proc. of the International Joint Conference on Neural Networks , pp. 2141–2146. Orlando: Wiley-IEEE Press.
• Schilling, M.; Cruse, H., & Schmitz, J. (2007). Mental models for cognitive control. In Arena, P.; Rodríguez-Vázquez, A.; Liñán-Cembrano, G. (Eds.): Bioengineered Bioinspired Systems III. Proceedings of the SPIE , pp. 1–14. Bellingham: SPIE.
• Arena, P.; Patanè, L.; Schilling, M., & Schmitz, J. (2007). Walking capabilities of Gregor controlled through Walknet. In Arena, P.; Rodríguez-Vázquez, A.; Liñán-Cembrano, G. (Eds.): Bioengineered Bioinspired Systems III., Proceedings of the SPIE , pp. 1–9. Bellingham: SPIE.

2006

• Schilling, M.; Patanè, L.; Arena, P.; Schmitz, J., & Schneider, A. (2006). Different, biomimetic inspired walking machines controlled by a decentralised control approach relying on artificial neural networks. In Proc. of SAB 2006 Workshop on Bio-inspired cooperative & adaptive behaviours in robots.

2005

• Heumer, G.; Schilling, M.; Latoschik, M.E. (2005). Automatic data exchange synchronization for knowledge-based intelligent virtual environments. In / (Ed.): Proc. of IEEE Virtual Reality , pp. 43–50. Bonn: Wiley-IEEE Press.

2003

• Latoschik, M.E., & Schilling, M. (2003). Incorporating VR Databases into AI Knowledge Representations: A Framework for Intelligent Graphics Applications. In Hamza, M.H. (Eds.): Proc. of 6. IASTED International Conference on Computer Graphics Imaging 2003 , pp. 79–84. Honolulu: ACTA Press.