Open-source Agile, Lego-like Robotics Stack 面向机器人敏捷与 Lego 式开发的开源栈

starVLA
An Agile, Lego-like Codebase for Embodied AI Engineering 面向具身智能敏捷与 Lego 式工程的开发代码库

Unify lego-like framework modules, training recipes, benchmark evaluation, and deployment interfaces so teams can iterate and ship faster. 统一 Lego 式框架模块、训练流程、基准评测与部署接口,让团队更快迭代并交付。

Design for fast iteration 为快速迭代而设计

From setup to benchmarking and deployment, the workflow is organized for fast, reliable team iteration. 从环境搭建到基准评测再到部署落地,整体流程围绕团队快速且可靠的迭代而设计。

Lego-like Modular Frameworks Lego 式模块化框架

StarVLA-FAST, StarVLA-OFT, StarVLA-PI, and StarVLA-GR00T share the same trainer, dataloader, and deployment stack for plug-and-play experimentation. StarVLA-FAST、StarVLA-OFT、StarVLA-PI、StarVLA-GR00T 共用训练、数据和部署管线,实现即插即用实验迭代。

Reproducible Benchmarks 可复现实验基准

Run SimplerEnv, LIBERO, RoboCasa, RoboTwin, and BEHAVIOR with benchmark-specific scripts and clear environment boundaries. 通过基准专用脚本和清晰环境边界,复现 SimplerEnv、LIBERO、RoboCasa、RoboTwin、BEHAVIOR。

Agile Delivery Loop 敏捷交付闭环

Use a unified WebSocket policy interface to bridge simulation and real-robot control without rewriting model serving logic. 使用统一 WebSocket 策略接口连接仿真与真机,无需重写模型服务逻辑。

Quick Start 快速开始

Choose a goal and copy the command templates. Evaluation workflows are split into two terminals to avoid dependency conflicts. 选择目标后复制命令模板。评测流程默认分为两个终端,以避免依赖冲突。

Terminal A
 
Terminal B
 

Reported benchmark snapshots 已公开基准快照

Numbers below are reported results from the official StarVLA docs and repository, with source links for verification. 以下数字来自 StarVLA 官方文档与仓库公开结果,并附带来源链接供核对。

Percent values reported in the benchmark summary chart. 以下为基准对比图中给出的百分比结果。

Benchmark 基准 starVLA Generalist starVLA 通用模型 Previous SOTA 此前 SOTA
LIBERO 97.8% 97.1%
LIBERO-Plus 79.7% 69.6%
SimplerEnv WidowX 70.2% 62.0%
SimplerEnv Google VA 73.8% 68.4%
SimplerEnv Google VM 79.3% 72.7%
RoboTwin Clean* 88.7% 82.7%
RoboTwin Random* 88.3% 76.8%
RoboCasa-GR1 57.3% 47.6%
RoboChallenge 30.0% 17.7%

Community and resources 社区与资源

Get help, follow live progress, and collaborate with researchers and engineers in the StarVLA ecosystem. 获取支持、跟进实时进展,并与 StarVLA 生态中的研究者和工程师协作。