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Embodied Neurorobotics

Living creatures, like walking animals, impress the observer by the elegance of their locomotion. They can also continuously learn and quickly adapt to new situations or solve specific tasks. They can even exploit their morphology to perform diverse complex autonomous behaviors, such as locomotion, object manipulation and transportation, and navigation, with a high degree of energy efficiency.

To achieve such complex behaviors for artificial walking systems, we focus on the Embodied Neurorobotics approach [1]. The approach not only (i) considers an integration of neural systems, body, and often the environment too, but also (ii) focuses on the detailed dynamical interactions of the neural computation (i.e., activity, plasticity, and memory at different time scales [2-4]) with the environment to realize and generalize complex autonomous behaviors. By doing so, we aim to understand the complex dynamical interactions between physical and computational components in embodied neural closed-loop systems.

Finally, we transfer our knowledge and development to other systems, such as service robots and exoskeletons, as well as to other application areas, such as service, construction, inspection, transportation, search and rescue, planetary exploration, and agriculture.

For more information, please contact professor, Poramate Manoonpong


[1] Manoonpong, P. and Tetzlaff, C. (2018) Editorial: Neural Computation in Embodied Closed-Loop Systems for the Generation of Complex Behavior: From Biology to Technology. Front. Neurorobot. 12:53. doi: 10.3389/fnbot.2018.00053

[2] Thor, M. and Manoonpong, P. (2019) Error-based Learning Mechanism for Fast Online Adaptation in Robot Motor Control, IEEE Transactions on Neural Networks and Learning Systems , doi: 10.1109/TNNLS.2019.2927737

[3] Aoi, S., Manoonpong, P., Ambe, Y., Matsuno, F. and Wörgötter, F. (2017) Adaptive Control Strategies for Interlimb Coordination in Legged Robots: A Review. Front. Neurorobot. 11:39. doi: 10.3389/fnbot.2017.00039

[4] Pitchai, M., Xiong, X., Thor, M., Billeschou, P., Lukas Mailander, P., Leung, B., Kulvicius, T., Manoonpong, P. (2019) CPG Driven RBF Network Control with Reinforcement Learning for Gait Optimization of a Dung Beetle-Like Robot. In: Tetko I., Kůrková V., Karpov P., Theis F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation. ICANN 2019. Lecture Notes in Computer Science, vol 11727. Springer, Cham

Last Updated 29.03.2023