# NeuroVLA **Repository Path**: nilbody_0/NeuroVLA ## Basic Information - **Project Name**: NeuroVLA - **Description**: No description available - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2026-02-12 - **Last Updated**: 2026-02-12 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

# NeuroVLA: A Brain-like Embodied Intelligence for Fluid and Fast Reflexive Robotics Control **Weiyu Guo**^1,4, **He Zhang**^1,4, **Pengteng Li**^1,4, **Tiefu Cai**^1,4, **Ziyang Chen**^1,4, **Yandong Guo**^1,4,
**He Xiao**⁴, **Yongkui Yang**³\*, **Ying Sun**^1,2\*, **Hui Xiong**^1,2\* ¹The Thrust of Artificial Intelligence, HKUST (Guangzhou), China ²The Department of CSE, HKUST, Hong Kong, China ³Shenzhen Institutes of Advanced Technology, CAS, China ⁴AI²Robotics, Shenzhen, China [![Nature](https://img.shields.io/badge/Nature-Under%20Review-E30613?style=flat-square)](https://github.com/guoweiyu/NeuroVLA) [![Python](https://img.shields.io/badge/Python-3.10%2B-3776AB?style=flat-square&logo=python&logoColor=white)](https://www.python.org/) [![License](https://img.shields.io/badge/License-AGPL%20v3-00599C?style=flat-square&logo=gnu-bash&logoColor=white)](https://www.gnu.org/licenses/agpl-3.0)

_{(a) Fine-grained Manipulation
Precision pouring task demanding high-frequency feedback for robotic stable control.}

_{(b) Temporal Memory
Multi-stage shaking task demonstrating phase tracking and working memory capabilities.}

_{(c) Reflexive Safety
Rapid withdrawal and recovery upon unexpected collision to ensure hardware safety.}

Real-world experiments evaluating precision (Pouring), memory (Shaking), and safety reflexes (Collision Recovery).

## 📖 Overview The pursuit of general-purpose embodied intelligence faces a critical **sensorimotor paradox**: traditional Vision-Language-Action (VLA) models suffer from "temporal blindness" and high latency, leading to action jitter and an inability to reflex instantaneously in dynamic scenarios. **NeuroVLA** introduces a bio-inspired, tri-level hierarchical architecture that restores the canonical division of labor found in biological motor systems. Instead of a monolithic processor, NeuroVLA decouples high-level cognition from low-level motor control: 1. **Cortical Module (Vision-Language):** Responsible for semantic planning and high-level goal generation. 2. **Cerebellar Module (Adaptive):** Functions as a high-frequency adaptive filter to predict sensory consequences and refine timing. 3. **Spinal Module (Spiking Neural Network):** Implements asynchronous, localized actuation and fast sensorimotor loops. By mapping the spinal module to event-driven spiking networks, NeuroVLA exploits temporal sparsity to minimize end-to-end latency, enabling localized, hardware-efficient learning on edge devices. ## 🔥 Key Results Our experiments on both simulated benchmarks and physical robotic hardware demonstrate distinctive capabilities that purely scaling monolithic VLAs cannot replicate: * **Kinematic Smoothness (75% Jerk Reduction):** The cerebellar module functions as an adaptive filter, effectively suppressing high-frequency intention tremor. This reduces kinematic jerk by over **75%**, ensuring fluid execution even with noisy visual feedback. * **Survival Reflexes (< 20 ms Latency):** Under unexpected physical collisions, the cerebellar-spinal loops trigger rapid withdrawal reflexes in **< 20 ms**, bypassing the prohibitive latency (> 200 ms) of the cortical loop to protect hardware. * **Emergent Sparsity:** The neuromorphic spinal layer exhibits unsupervised functional self-organization without explicit training signals: * *Temporal Sparsity:* Neurons spontaneously revert to quiescence during static posturing to minimize metabolic cost. * *Spatial Disentanglement:* The network naturally segregates high-dimensional control signals into distinct, somatotopic behavioral modes. ## 🛠️ Installation The environment setup is based on standard VLA dependencies. We recommend using `conda` to manage the environment. ### Prerequisites * Linux (Ubuntu 20.04/22.04 recommended) * Python 3.10+ * NVIDIA GPU with CUDA support ### Environment Setup ```bash # 1. Create a conda environment conda create -n neurovla python=3.10 -y conda activate neurovla # 2. Install PyTorch (Adjust CUDA version as needed) conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia # Install requirements pip install -r requirements.txt # Install FlashAttention2 pip install flash-attn --no-build-isolation ``` Note: For specific dependency versions and detailed configuration related to the base VLA framework, please refer to the [StarVLA](https://github.com/starVLA/starVLA) Environment Setup Guide. Our implementation builds upon these foundational libraries. ## 🚀 Usage ```bash # 1. Run training example bash NeuroVLA/scripts/run_scripts/run_libero_train_NeuroVLA.sh # 2. Run evaluation example bash NeuroVLA/examples/LIBERO/eval_libero.sh ``` ## 📝 Citation If you find our code or architecture helpful in your research, please cite our repository: ```bibtex @misc{guo2025neurovla, author = {Guo, Weiyu and Zhang, He and Li, Pengteng and Cai, Tiefu and Chen, Ziyang and Guo, Yandong and Xiao, He and Yang, Yongkui and Sun, Ying and Xiong, Hui}, title = {NeuroVLA: A Brain-like Embodied Intelligence for Fluid and Fast Reflexive Robotics Control}, year = {2025}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {https://github.com/guoweiyu/NeuroVLA}} } ``` ## 🛡️ License This project is licensed under the GNU Affero General Public License v3.0 (AGPL-3.0). This is a strict copyleft license. If you use this software (or a modified version of it) to provide a service over a network, you must make the source code available to the users of that service. See LICENSE for more details.