aiqbench analysis
This arXiv preprint proposes a brain-inspired vision-language-action architecture that pairs a language-model-based planner with cerebellar-style modulation and a spiking controller on neuromorphic hardware. The reported gains, a 75.6% reduction in commanded jerk and a 54.8% collision-recovery rate versus 0% for the baselines, come from simulated and physical tests whose scope and baseline choices matter for judging generality. For AI readers, the more durable signal is the energy and latency profile (2.19 ms, 0.87 mJ per inference) and the argument that high-frequency feedback and motor memory need explicit architectural support beyond language-model design.
NeuroVLA: A Brain-inspired Embodied Intelligence for Fluid and Fast Reflexive Robotics Control
arXiv:2601.14628v2 Announce Type: replace-cross Abstract: Vision-language-action (VLA) models inherit powerful semantic representations from large language models, but carrying over their design priorities can leave the demands of physical control under-specified. Stable manipulation requires high-frequency proprioceptive feedback and persistent motor state, in addition to instruction understanding. Here we introduce Neuromorphic Vision-Language-Action (NeuroVLA), a brain-inspired architecture that couples a cortical vision-language planner to recurrent cerebellar modulation and a stateful spiking spinal controller. These components translate semantic intent into actions while incorporating high-frequency proprioceptive and force feedback, maintaining short-term motor memory, and enabling local responses to unexpected contact. In simulated and physical manipulation tasks, the cerebellar module reduces mean commanded jerk by 75.6%, and NeuroVLA achieves a 54.8% collision-recovery rate compared with 0.0% for the evaluated VLA baselines. The spinal controller implemented on an FPGA-based neuromorphic processor achieves 2.19 ms inference latency and 0.87 mJ per inference, corresponding to approximately 0.4 W during inference. These results motivate moving beyond inherited language-model design priorities toward embodied architectures that explicitly integrate semantic planning, high-frequency bodily feedback, motor memory, and local correction.
Abstract:Vision-language-action (VLA) models inherit powerful semantic representations from large language models, but carrying over their design priorities can leave the demands of physical control under-specified. Stable manipulation requires high-frequency proprioceptive feedback and persistent motor state, in addition to instruction understanding. Here we introduce Neuromorphic Vision-Language-Action (NeuroVLA), a brain-inspired architecture that couples a cortical vision-language planner to recurrent cerebellar modulation and a stateful spiking spinal controller. These components translate semantic intent into actions while incorporating high-frequency proprioceptive and force feedback, maintaining short-term motor memory, and enabling local responses to unexpected contact. In simulated and physical manipulation tasks, the cerebellar module reduces mean commanded jerk by 75.6%, and NeuroVLA achieves a 54.8% collision-recovery rate compared with 0.0% for the evaluated VLA baselines. The spinal controller implemented on an FPGA-based neuromorphic processor achieves 2.19 ms inference latency and 0.87 mJ per inference, corresponding to approximately 0.4 W during inference. These results motivate moving beyond inherited language-model design priorities toward embodied architectures that explicitly integrate semantic planning, high-frequency bodily feedback, motor memory, and local correction.
14 pages, 8 figures. Added NeuroVLA to the title; clarified language-model design inertia, high-frequency proprioception, motor memory and local feedback; revised biological analogies; corrected figure captions and references. Experimental figures and main quantitative results are unchanged
Robotics (cs.RO); Artificial Intelligence (cs.AI)
arXiv:2601.14628 [cs.RO]
(or arXiv:2601.14628v2 [cs.RO] for this version)
Full text under the publisher's open licence · 220 words
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