Publikationen

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

2023

• Simmering, J., Hermes, L., Schneider, A., & Schilling, M. (2023). Adaptation of a Decentralized Controller to Curve Walking in a Hexapod Robot. In Cascalho, J., Tokhi, M., Silva, M., Mendes, A., Goher, K., & Funk, M. (Hrsg.), Robotics in Natural Settings (S. 264–275). 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 al., G. N. (Hrsg.), Lecture Notes in Computer ScienceProc. of 8th International Online & Onsite Conference on Machine Learning, Optimization, and Data Science (S. 1–16). Springer Nature. doi: 10.1007/978-3-031-25891-6_20.
• Hermes, L., Liuliakov, A., & Schilling, M. (2023). Graph Learning by Dynamic Sampling. In Verma, B., & Kasabov, N. K. (Hrsg.), IJCNN 2023 Conference Proceedings (S. 1–8). Wiley-IEEE Computer Society Press. doi: 10.1109/IJCNN54540.2023.10191846.
• Mindlin, D., Schilling, M., & Cimiano, P. (2023). ABC-GAN: Spatially Constrained Counterfactual Generation for Image Classification Explanations. In Longo, L. (Hrsg.), Explainable Artificial Intelligence (S. 260–282). Springer. doi: 10.1007/978-3-031-44064-9_15.
• Schilling, M., & Cruse, H. (2023). neuroWalknet, a controller for hexapod walking allowing for context dependent behavior. PLoS Computational Biology, 19 (1), Artikel 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.

2022

• Schilling, & M. (2022). Coding Challenge. In Gerick, J., A. Sommer, A., & Zimmermann, G. (Hrsg.), Kompetent Prüfungen gestalten – 60 Prüfungsformate für die Hochschullehre (S. 108–111). 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. (Hrsg.), Proceedings of the International Joint Conf. on Neural Networks (S. 1–8). 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, P., & Wortman Vaughan, J. (Hrsg.), Proceedings of the NeurIPS 2021 Competitions Demonstrations Track, PMLR (S. 97–112). Curran Associates.
• Schilling, M., 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.

2021

• Schilling, M., & Burgahn, C. R. (2021). Evaluation of Graph Convolutions for Spatio-Temporal Predictions of EV-Charge Availability. In / (Hrsg.), Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI 2021) (S. 1–6). Wiley-IEEE Press. doi: 10.1109/SSCI50451.2021.9660162.
• Hermes, L., Hammer, B., & Schilling, M. (2021). Application of Graph Convolutions in a Lightweight Model for Skeletal Human Motion Forecasting. In Verleysen, M. (Hrsg.), Proceedings of 29th European Symposium on Artificial Neural Networks (S. 111–116). i6doc.
• Schilling, M., Melnik, A., Ohl, F. W., Ritter, H., & Hammer, B. (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. (Hrsg.), 2021 IEEE Symposium Series on Computational Intelligence (SSCI) (S. 01–09). 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. (Hrsg.), Artificial Neural Networks and Machine Learning -- ICANN 2021. 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14--17, 2021, Proceedings, Part IV (S. 638–649). 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. (Hrsg.), Optimization and Learning. 4th International Conference, OLA 2021, Catania, Italy, June 21-23, 2021, Proceedings (S. 115–131). Springer International Publishing. doi: 10.1007/978-3-030-85672-4_9.

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, M., Konen, K., Ohl, F.W., & Korthals, T. (2020). Decentralized Deep Reinforcement Learning for a Distributed Adaptive Locomotion Controller of a Hexapod Robot. In / (Hrsg.), Proceedings of IEEE/RSJ International Conference on Intelligent Robots Systems (S. 5335–5342). Wiley-IEEE Press.
• Schilling, M., 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. (Hrsg.), Proceedings of the 8th IEEE RAS/EMBS International Conference for Biomedical Robotics Biomechatronics (BioRob) (S. 946–953). 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., & al., e. (Hrsg.), Lecture Notes in Computer Science (S. 617–630). Springer.
• Schilling, M. 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.

2019

• Schilling, M., W., B., Muelling, K., & Wrede, B. 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.
• 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 / (Hrsg.), Proceedings of the 2019 IEEE International Conference on Systems, Man, Cybernetics (S. 1472–1478). Wiley-IEEE Press. doi: 10.1109/SMC.2019.8914302.
• Schilling, M. (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 / (Hrsg.), Proc. of the International Joint Conference on Neural Networks 2019 (S. 1–8). Wiley-IEEE Press.
• Schilling, & M. (2019). Hierarchical Dual Quaternion-Based Recurrent Neural Network as a Flexible Internal Body Model. In / (Hrsg.), Proc. of the International Joint Conference on Neural Networks 2019 (S. 1–8). Wiley-IEEE Press.
• Korthals, T., Schilling, M., & Leitner, J. (2019). A Perceived Environment Design using a Multi-Modal Variational Autoencoder for learning Active-Sensing. doi: 10.48550/arXiv.1911.00584.
• Melnik, A., Fleer, S., Schilling, M., & Ritter, H. (2019). Modularization of End-to-End Learning: Case Study in Arcade Games.

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. (Hrsg.), Advances in Intel. Systems Computing (S. 272–282). 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. (Hrsg.), The NIPS 2017 Competition: Building Intelligent Systems (S. 121–154). Springer.
• Schilling, & M (2018). Coding Challenge. In Gerick, J., Sommer, A., & Zimmermann, G. (Hrsg.), Kompetent Prüfungen gestalten -- 53 Prüfungsformate für die Hochschullehre (S. 42–45). Waxmann.
• Schilling, M., & Cruse, H. (2018). Getting cognitive. In Bläsing, B., Puttke, M., & Schack, T. (Hrsg.), The Neurocognition of Dance. Mind, movement and motor skills (S. 150–168). Routledge.
• Cruse, H., & Schilling, M. (2018). Pattern Generation. In Prescott, T., Lepora, N., & Verschure, P. (Hrsg.), Living Machines. A handbook of research in biomimetics and biohybrid systems (S. 218–226). 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, 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. e. (Hrsg.), Proceedings of the Artificial Intelligence and Simulation of Behaviour Conference (AISB 2017) (S. 286–292). Curran Associates.

2016

• Nomikou, I., Schilling, M., & Heller, V. 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.
• 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., Mura, A., Mangan, M., Verschure, P., Desmulliez, M., & Prescott, T. (Hrsg.), Proceedings of the 5th International Conference on Living Machines (S. 239–250). 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 / (Hrsg.), Proceedings of the AAAI Fall Symposium Series 2016, Stanford (USA) (S. 338–344). AAAI Press.
• Cruse, H., & Schilling, M. (2016). Mental states as emergent properties. From walking to consciousness. In Metzinger, T., & Windt, J. (Hrsg.), Open Mind, Philosophy and the Mind Sciences in the 21st Century. Vol 1 (S. 349–386). MIT Press.
• Lücking, P., Rohlfing, K., Wrede, B., & Schilling, M. (2016). Preschoolers' engagement in social interaction with an autonomous robotic system. In / (Hrsg.), 2016 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) (S. 210–216). Wiley-IEEE Press. doi: 10.1109/devlrn.2016.7846821.
• 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, 113 (27), E3811–E3811. doi: 10.1073/pnas.1607146113.

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 / (Hrsg.), Proceedings IEEE International Conference on Robotics and Automation (ICRA) (S. 3140–3145). 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 / (Hrsg.), ICRA 2015 CPG Workshop : CPGs for Locomotion Control: Pros, Cons & Alternatives (S. 1–3). Wiley-IEEE Press.
• Baum, M., Meier, M., & Schilling, M. (2015). Population based Mean of Multiple Computations networks: A building block for kinematic models. In / (Hrsg.), 2015 International Joint Conference on Neural Networks (IJCNN) (S. 3616–3623). 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. (Hrsg.), Proceedings of the 19th Workshop on the Semantics and Pragmatics of Dialogue (S. 206–207). University of Gothenburg.
• Cruse, H., & Schilling, M. (2015). The Bottom-Up Approach: Benefits and Limits. 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 / (Hrsg.), 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. (Hrsg.), Proceedings of the 3rd Conference on Biomimetics and Biohybrid Systems (S. 427–430). Springer.
• Cruse, H., & Schilling, M. (2014). Action Selection within short time windows. In Duff, A., Prescott, T., Verschure, P., & Lepora, N. (Hrsg.), Proceedings of Biomimetic and Biohybrid Systems: Third International Conference, Living Machines 2014, Milan, Italy (S. 47–58). 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. (Hrsg.), Routledge Companion to Sport and Exercise Psychology:Global Perspectives and Fundamental Concepts (S. 306–321). 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., & Narayanan, S. (2013). Communicating with Executable Action Representations. In / (Hrsg.), Proceedings of AAAI Spring Symposium Series 2013, Stanford (S. 1–8). AAAI Press.
• 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.

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, 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 / (Hrsg.), Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (S. 2762–2768). 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 / (Hrsg.), Proceedings of the International Joint Conference on Neural Networks 2012 (S. 1–8). Wiley-IEEE Press. doi: 10.1109/ijcnn.2012.6252846.
• Schilling, M., 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. (Hrsg.), Language Grounding in Robots (S. 131–150). Springer.

2011

• Schilling, & M (2011). Learning by seeing---associative learning of visual features through mental simulation of observed action. In Dourest, R. e. (Hrsg.), Proc. of the ECAL 2011, Paris (S. 731–738). 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 / (Hrsg.), Proceedings of the International Joint Conference on Neural Networks 2011, San Jose (CA) (S. 2836–2843). 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. (Hrsg.), Advances in Artificial Life, ECAL 2011. Proceed. of the Eleventh Europ. Conf. on the Synthesis and Simulation of Living Systems (S. 185–192). MIT Press.
• 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.

2010

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

2009

• Schilling, & M (2009). Dynamic equations in MMC networks: Construction of a dynamic body model. In / (Hrsg.), Proceedings of the 12th International Conference on Climbing and Walking Robots (S. 1047–1054). 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. (Hrsg.), Proceedings of the 12th International Conference on Climbing and Walking Robots (S. 623–630). World Scientific Publishing.
• Cruse, H., Dürr, V., Schilling, M., & Schmitz, J. (2009). A Bottom-up approach for cognitive control. In Arena, P., & Patané, L. (Hrsg.), Spatial temporal patterns for action-oriented perception in roving robots (S. 179–218). Springer. doi: 10.1007/978-3-540-88464-4_4.
• 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.

2008

• Schilling, M., Schneider, A., Cruse, H., & Schmitz, J. (2008). Local control mechanisms in six-legged walking. In / (Hrsg.), Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (S. 2655–2660). Wiley-IEEE Press. doi: 10.1109/iros.2008.4650591.
• 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. (Hrsg.), Modeling Communication with Robots and Virtual Humans (S. 77–108). Springer.

2007

• Schilling, M., & Cruse, H. (2007). Hierarchical MMC networks as a manipulable body model. In / (Hrsg.), Proc. of the International Joint Conference on Neural Networks (S. 2141–2146). Wiley-IEEE Press.
• Schilling, M., & Cruse, H. J. (2007). Mental models for cognitive control. In Arena, P., Rodríguez-Vázquez, A., & Liñán-Cembrano, G. (Hrsg.), Bioengineered Bioinspired Systems III. Proceedings of the SPIE (S. 1–14). 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. (Hrsg.), Bioengineered Bioinspired Systems III., Proceedings of the SPIE (S. 1–9). SPIE.
• 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.

2006

• Schilling, M., Patanè, L., Arena, P., & Schmitz, J. 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 / (Hrsg.), Proc. of IEEE Virtual Reality (S. 43–50). 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. (Hrsg.), Proc. of 6. IASTED International Conference on Computer Graphics Imaging 2003 (S. 79–84). ACTA Press.