NVIDIA Alpamayo 1 Nano is an open 10B reasoning VLA model for autonomous vehicles that pairs driving trajectories with Chain-of-Causation reasoning.

[ICLR 2026] SimpleVLA-RL: Scaling VLA Training via Reinforcement Learning

A Pragmatic VLA Foundation Model

Dexbotic: Open-Source Vision-Language-Action Toolbox

[RSS 2025] Learning to Act Anywhere with Task-centric Latent Actions

InternRobotics' open platform for building generalized navigation foundation models.

Unified Codebase for Advanced World Models.

[NeurIPS 2025 spotlight] Official implementation for "FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving"

🚀🚀🚀A collection of some awesome public projects about Large Language Model(LLM), Vision Language Model(VLM), Vision Language Action(VLA), AI Generated Content(AIGC), the related Datasets and Applications.

🔥 SpatialVLA: a spatial-enhanced vision-language-action model that is trained on 1.1 Million real robot episodes. Accepted at RSS 2025.

Running VLA at 30Hz frame rate and 480Hz trajectory frequency

[ICLR2026] Official implementation for "JanusVLN: Decoupling Semantics and Spatiality with Dual Implicit Memory for Vision-Language Navigation"

DeepThinkVLA: Enhancing Reasoning Capability of Vision-Language-Action Models

VLA-0: Building State-of-the-Art VLAs with Zero Modification

FlashRT is a high-performance realtime inference engine for small-batch, latency-sensitive AI workloads. The flagship integration is production VLA control for Pi0, Pi0.5, GROOT N1.6, and Pi0-FAST. Also support llm e.g, qwen3.6-27B

🌐 Vision-Language-Action Models for Autonomous Driving: Past, Present, and Future

[CVPR 2025, Spotlight] SimLingo (CarLLava): Vision-Only Closed-Loop Autonomous Driving with Language-Action Alignment

本项目旨在为致力于进入VLA(Vision-Language-Action)领域的算法工程师提供一份全中文、实战导向的学习/面试手册。 不同于通用的 CV/NLP 面试指南，本项目聚焦于 Robotics 特有的挑战

📚这个仓库是在arxiv上收集的有关VLN，VLA，World Model，SLAM，Gaussian Splatting,非线性优化等相关论文。每天都会自动更新！issue区域是最新10篇论文

Control AI robots. Community-driven UI middleware for controlling robots, recording datasets, training action models. Compatible with SO-100 and SO-101