2020/06/08 by Ioannis Polykretis, Konstantinos P. Michmizos, Polykretis, Ioannis +1
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Neuroscience · #Advanced Memory and Neural Computing #FOS: Biological sciences #FOS: Computer and information sciences #Neural and Evolutionary Computing (cs.NE) #Neural dynamics and brain function #Neurons and Cognition (q-bio.NC) #Neuroscience and Neural Engineering #Robotics (cs.RO) #cs.NE #cs.RO #q-bio.NC
paper · pdf · doi:10.48550/arxiv.2006.04765
8 pages, 7 figures, International Conference on Neuromorphic Systems (ICONS) 2020
arxiv created 2020/06/08 · openalex publication_date 2020/06/08 · arxiv updated 2020/06/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Locomotion is a crucial challenge for legged robots that is addressed "effortlessly" by biological networks abundant in nature, named central pattern generators (CPG). The multitude of CPG network models that have so far become biomimetic robotic controllers is not applicable to the emerging neuromorphic hardware, depriving mobile robots of a robust walking mechanism that would result in inherently energy-efficient systems. Here, we propose a brain-morphic CPG controler based on a comprehensive spiking neural-astrocytic network that generates two gait patterns for a hexapod robot. Building on the recently identified astrocytic mechanisms for neuromodulation, our proposed CPG architecture is seamlessly integrated into Intel's Loihi neuromorphic chip by leveraging a real-time interaction framework between the chip and the robotic operating system (ROS) environment, that we also propose. Here, we demonstrate that a Loihi-run CPG can be used to control a walking robot with robustness to sensory noise and varying speed profiles. Our results pave the way for scaling this and other approaches towards Loihi-controlled locomotion in autonomous mobile robots.