36Kr reports on Zhipingfang's (智平方) NeuroVLA, a Vision-Language-Action (VLA) model for robots that takes inspiration from the human nervous system — specifically the cortex-cerebellum-spinal cord hierarchy. The standout: end-side robot reaction time hits 20ms, fast enough for real-time physical interaction.
The "cortex-cerebellum-spinal cord" architecture: NeuroVLA has three coupled components, modeled after the human nervous system. (1) The "cortex" — a large VLM that handles high-level reasoning ("what does the user want?"). (2) The "cerebellum" — a mid-sized model that handles motion planning ("how should the arm move?"). (3) The "spinal cord" — a tiny model (just 50M parameters) that handles low-level control ("send these motor commands"). The three components run in a hierarchical loop, with the spinal cord running at 100 Hz and the cortex at 1 Hz.
The "20ms reaction time" highlight: the spinal cord's 50M model runs on the robot's edge GPU at 100 Hz, giving a 10ms control loop. The end-to-end reaction time (from sensor input to motor output) is 20ms, fast enough for safe physical interaction with humans.
The benchmark: on a set of robot manipulation tasks (pick-and-place, assembly, tool use), NeuroVLA scores 84.3, on par with the best closed-source VLA models (Google RT-2, Tesla Optimus). The "20ms reaction time" is a 3-5× improvement over previous VLA models, which typically have 50-100ms reaction times.
The bigger takeaway: "hierarchical VLA" is the right architecture for real-time robot control. The "one big model does everything" approach is too slow, and the "hierarchical" approach (cortex for reasoning, spinal cord for control) is significantly more efficient. For the industry, this signals that "VLA architecture" will move to hierarchical designs, and the next round of robot AI will be defined by "how fast the spinal cord runs."