A Brain-inspired Embodied Intelligence for Fluid and Fast Reflexive Robotics Control
Weiyu Guo, He Zhang, Pengteng Li, Tiefu Cai, Ziyang Chen, Yandong Guo, Xiao He, Yongkui Yang, Ying Sun, Hui Xiong

TL;DR
This paper introduces NeuroVLA, a bio-inspired neuromorphic framework for robotic control that achieves rapid, stable, and reflexive actions with minimal energy, mimicking biological nervous system functions.
Contribution
The paper presents the first neuromorphic vision-language-action system deployed on physical robots, demonstrating biological motor traits and real-time reflexes without extra data.
Findings
Achieves stable robotic arm control and safety reflexes in under 20 ms
Consumes only 0.4W energy on neuromorphic hardware
Exhibits biological-like temporal memory and reflex responses
Abstract
Recent advances in embodied intelligence have leveraged massive scaling of data and model parameters to master natural-language command following and multi-task control. In contrast, biological systems demonstrate an innate ability to acquire skills rapidly from sparse experience. Crucially, current robotic policies struggle to replicate the dynamic stability, reflexive responsiveness, and temporal memory inherent in biological motion. Here we present Neuromorphic Vision-Language-Action (NeuroVLA), a framework that mimics the structural organization of the bio-nervous system between the cortex, cerebellum, and spinal cord. We adopt a system-level bio-inspired design: a high-level model plans goals, an adaptive cerebellum module stabilizes motion using high-frequency sensors feedback, and a bio-inspired spinal layer executes lightning-fast actions generation. NeuroVLA represents the…
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Taxonomy
TopicsAdvanced Memory and Neural Computing · Neural Networks and Reservoir Computing · Robotic Locomotion and Control
