J. Camilo Vasquez Tieck

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Bio

Camilo is since 2015 a research scientist at FZI Research Center for Computer Science and a PhD student on the field of neurorobotics at (KIT) with Prof. Rüdiger Dillmann in Karlsruhe, Germany. He works on motion representation for manipulation and grasping with spiking neural networks and works in the SP10 Neurorobotics of the Human Brain Project.

He is a very curious and open minded engineer, with experience and a special interest in Mechanical Design, Robotics and Software Development. Strong analytical skills in math and physics. Good at modeling situations, creating algorithms and programming. Can adapt easily to changes and new technologies; will interact without difficulty with new people and environments. A pacific person. Creative in the face of problems and difficulties.

He finished his MSc degree in Informatics (2015) at the the Karlsruhe Institute of technology (KIT), with focus on cognitive systems and humanoid robots. He's master thesis was titled "Generic Framework for the Mapping of Human Motion Data on Humanoid Robots". He worked one year (2011) doing research on interactive digital television at Artica in the EAFIT University in Medellin, Colombia. From 2007 to 2010 he worked as solution developer in the fields of biomechanics and telecommunications at Ilimitada S.A in Medellin, Colombia. He worked one year (2006) designing, developing and building robots for protein christalization at Formulatrix Inc. in Boston, USA. He finished the studies as mechanical engineer (2007) with a focus on product development and control engineering, and as computer scientist (2010) with a focus on software engineering and mechatronics, both programs at the EAFIT University in Medellin, Colombia.

Publications

2020

Tieck, J. C. V., Secker, K., Kaiser, J., Roennau, A., & Dillmann, R. (2020). Soft-grasping with an anthropomorphic robotic hand using spiking neurons. IEEE Robotics and Automation Letters. https://doi.org/10.1109/LRA.2020.3034067
Tieck, J. C. V., Weber, S., Stewart, T. C., Kaiser, J., Roennau, A., & Dillmann, R. (2020). A spiking network classifies human sEMG signals and triggers finger reflexes on a robotic hand. Robotics and Autonomous Systems, 131, 103566. https://doi.org/10.1016/j.robot.2020.103566
Steffen, L., da Silva, R. K., Ulbrich, S., Tieck, J. C. V., Roennau, A., & Dillmann, R. (2020). Networks of place cells for representing 3D environments and path planning. 2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob), 1158–1165.
Kaiser, J., Friedrich, A., Tieck, J. C. V., Reichard, D., Roennau, A., Neftci, E., & Dillmann, R. (2020). Embodied neuromorphic vision with continuous random backpropagation. 2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob), 1202–1209.
Delbruck, T., Elfadel, I. A. M., Muzaffar, S., Haessig, G., Wang, B., Bermak, A., Graca, R., Camunas-Mesa, L., Senevirathna, B., Abshire, P., Linares-Barranco, B., Afshar, S., Liu, S.-C., Wang, R. M., Dudek, P., Carey, S., de la Rosa, J., Dandin, M., Lu, S., Tieck, J. C. V., … Leon-Salas, W. D. (2020). Lessons Learned the Hard Way. 2020 IEEE International Symposium on Circuits and Systems (ISCAS), 1–18. https://doi.org/10.1109/ISCAS45731.2020.9180983

2019

Tieck, J. C. V., Steffen, L., Kaiser, J., Reichard, D., Roennau, A., & Dillmann, R. (2019). Combining motor primitives for perception driven target reaching with spiking neurons. Cognitive Informatics and Natural Intelligence (IJCINI), 13(1), 12. https://doi.org/10.4018/IJCINI.2019010101
Tieck, J. C. V., Rutschke, J., Kaiser, J., Schulze, M., Buettner, T., Reichard, D., Roennau, A., & Dillmann, R. (2019). Combining spiking motor primitives with a behavior-based architecture to model locomotion for six-legged robots. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS ).
Tieck, J. C. V., Schnell, T., Kaiser, J., Mauch, F., Roennau, A., & Dillmann, R. (2019). Generating pointing motions for a humanoid robot by combining motor primitives. Frontiers in Neurorobotics, 13, 77.
Tieck, J. C. V., Becker, P., Kaiser, J., Peric, I., Akl, M., Reichard, D., Roennau, A., & Dillmann, R. (2019). Learning target reaching motions with a robotic arm using brain-inspired dopamine modulated STDP. 2019 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI* CC).
Tieck, J. C. V., Kaiser, J., Steffen, L., Schulze, M., Arnim, A. V., Reichard, D., Roennau, A., & Dillmann, R. (2019). The neurorobotics platform for teaching – embodiment experiments with spiking neural networks and virtual robots. 2019 IEEE International Conference on Cyborg and Bionic Systems (CBS).
Kaiser, J., Friedrich, A., Tieck, J. C. V., Reichard, D., Roennau, A., Neftci, E., & Dillmann, R. (2019). Embodied event-driven random backpropagation. ArXiv Preprint ArXiv:1904.04805. https://arxiv.org/abs/1904.04805
Kaiser, J., Friedrich, A., Tieck, J. C. V., Reichard, D., Roennau, A., Neftci, E., & Dillmann, R. (2019). Embodied neuromorphic vision with event-driven random backpropagation. ArXiv Preprint ArXiv:1904.04805.
Kaiser, J., Hoff, M., Konle, A., Tieck, J. C. V., Kappel, D., Reichard, D., Subramoney, A., Legenstein, R., Roennau, A., Maass, W., & Dillmann, R. (2019). Embodied synaptic plasticity with online reinforcement learning. Frontiers in Neurorobotics.

2018

Tieck, J. C. V., Steffen, L., Kaiser, J., Reichard, D., Roennau, A., & Dillmann, R. (2018). Controlling a robot arm for target reaching without planning using spiking neurons. In 2018 IEEE 17th International Conference on Cognitive Informatics & Cognitive Computing (ICCI* CC) (pp. 111–116).
Tieck, J. C. V., Weber, S., Stewart, T. C., Roennau, A., & Dillmann, R. R. (2018). Triggering robot hand reflexes with human EMG data using spiking neurons. International Conference on Intelligent Autonomous Systems IAS-15, 902–916. https://doi.org/10.1007/978-3-030-01370-7_70
Tieck, J. C. V., Steffen, L., Kaiser, J., Arne, R., & Dillmann, R. (2018). Multi-modal motion activation for robot control using spiking neurons. Biomedical Robotics and Biomechatronics (BioRob), 2018 IEEE International Conference On.
Kaiser, J., Melbaum, S., Tieck, J. C. V., Roennau, A., Butz, M. V, & Dillmann, R. (2018). Learning to reproduce visually similar movements by minimizing event-based prediction error. In 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob) (pp. 260–267).
Tieck, J. C. V., Pogančić, M. V., Kaiser, J., Roennau, A., Gewaltig, M.-O., & Dillmann, R. (2018). Learning Continuous Muscle Control for a Multi-joint Arm by Extending Proximal Policy Optimization with a Liquid State Machine. In International Conference on Artificial Neural Networks ICANN 2018 (pp. 211–221). https://doi.org/10.1007/978-3-030-01418-6_21
Kaiser, J., Weinland, J., Keller, P., Steffen, L., Tieck, J. C. V., Reichard, D., … Dillmann, R. (2018). Microsaccades for Neuromorphic Stereo Vision. In International Conference on Artificial Neural Networks ICANN 2018 (pp. 244–252).
Kaiser, J., Lindner, G., Tieck, J. C. V., Schulze, M., Hoff, M., Roennau, A., & Dillmann, R. (2018). Microsaccades for asynchronous feature extraction with spiking networks. In International Conference on Development and Learning and Epigenetic Robotics (ICDL-EPIROB).

2017

Tieck, J. C. V., Donat, H., Kaiser, J., Peric, I., Ulbrich, S., Roennau, A., … Dillmann, R. (2017). Towards grasping with spiking neural networks for anthropomorphic robot hands. International Conference on Artificial Neural Networks ICANN 2017, 10613 LNCS, 43–51. https://doi.org/10.1007/978-3-319-68600-4_6
Kaiser, J., Zimmerer, D., Tieck, J. C. V., Ulbrich, S., Roennau, A., & Dillmann, R. (2017). Spiking convolutional deep belief networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10614 LNCS, pp. 3–11). https://doi.org/10.1007/978-3-319-68612-7_1
Falotico, E., Vannucci, L., Ambrosano, A., Albanese, U., Ulbrich, S., Tieck, J. C. V., … Gewaltig, M. O. (2017). Connecting artificial brains to robots in a comprehensive simulation framework: The neurorobotics platform. Frontiers in Neurorobotics, 11(JAN). https://doi.org/10.3389/fnbot.2017.00002

2016

Kaiser, J., Tieck, J. C. V., Hubschneider, C., Wolf, P., Weber, M., Hoff, M., … Zollner, J. M. (2016). Towards a framework for end-to-end control of a simulated vehicle with spiking neural networks. In 2016 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots, SIMPAR 2016 (pp. 127–134). https://doi.org/10.1109/SIMPAR.2016.7862386

Teaching

Projects

flexible and addaptive motion planning pipeline for assembly with industrial robotic arm, AI data processing, C++, ROS MoveIt, ros_control, rviz, behaviour trees
mesh generation based on 3D air radar data, Python, pdal, Blender
web application for autonomous shuttle service, angular, materialize, Python
mixed reality demonstrator for autonomous vehicles
NRP backend, python, gazebo
motion mapping, ARMAR robot, C++, zeroICE, Levenberg-Marquardt optimization
motion database, C++, mongoDB, zeroICE
Interactive digital television
iPlate 1 and 2, C#, mechanical design, prototyping, build
Biomechanics software suite with hardware interface, C#
MobileSync with email, MiBox, C#
Rock Imager, 182 design, CNC, RhinoCAM

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