Overview Research

The Autonomous Intelligent Systems group has two main research directions (more details will be added soon): First, one focus is on methods for adaptive motor control and (Deep) Reinforcement Learning, in particular, of locomotion in legged robots. Second, we work on predictive Machine Learning models –as are recurrent neural networks or, more recently, graph neural networks– that allow and facilitate planning ahead or anticipate actions of other agent as required for control of interactive and intelligent systems.

If you are interested in a Master or Bachelor thesis, just drop us a mail and get in contact.

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

• 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.
• 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.

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., & 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., 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. 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.
• 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.
• 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.

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

• 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.
• 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., & 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.