In the past few decades, bioinspired hexapod walking robots have attracted increasing attention, mainly due to their potential to efficiently traverse rough terrains. Recently, neuromorphic (brain-inspired) robotic control has been shown to outperform conventional control paradigms in stochastic environments. In this work, we propose a neuromorphic adaptive body leveling algorithm for a hexapod walking robot during transversal over multi-leveled terrain. We demonstrate adaptive control with distributed accelerator-driven neuro-integrators with only a few thousand spiking neurons. We further propose a framework for the integration of MuJoCo, a modeling environment, and Nengo, a spiking neural networks compiler, for efficient evaluation of neuromorphic control over high degrees of freedom robotic systems in realistic physics-driven scenarios.
|Title of host publication||BioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|State||Published - 2021|
|Event||2021 IEEE Biomedical Circuits and Systems Conference, BioCAS 2021 - Virtual, Online, Germany|
Duration: 6 Oct 2021 → 9 Oct 2021
|Name||BioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings|
|Conference||2021 IEEE Biomedical Circuits and Systems Conference, BioCAS 2021|
|Period||6/10/21 → 9/10/21|
Bibliographical noteFunding Information:
The authors would like to thank the students and faculty of the NBEL for the discussions.
© 2021 IEEE.
- Neural engineering framework
- spiking neural networks