EVA-Client：面向真实机器人具身策略的统一数据采集、推理与部署框架

摘要

我们推出EVA-Client，这是一个开源框架，用于在真实机器人上部署、采集数据并评估经过训练的操控策略。EVA-Client作为策略服务器与物理硬件之间的桥梁，将策略迭代循环中的真实机器人阶段统一于单一代码库中。它做出三项贡献：首先，采用组件解耦架构，机器人后端、推理策略与传输中间件构成正交网格——新增机器人或策略仅影响其对应层级；其次，通过调试、采集与评估工作流实现可检查的执行，模式涵盖开环仿真到连续实时控制；第三，每次评估运行同时完成数据采集，以训练就绪格式记录完整回放过程，附带详尽日志及并列对比查看器，使每次评估为下一轮训练提供输入，而非沦为未记录的印象。EVA-Client进一步将主要实时推理策略（同步与异步执行、ACT风格时序集成、实时分块处理及朴素异步消融基线）整合至单一配置界面背后。

English

We present EVA-Client, an open-source framework for deployment, data collection, and evaluation of trained manipulation policies on real robots. Sitting between a policy server and the physical hardware, EVA-Client unifies the real-robot stages of the policy iteration loop within a single codebase. It makes three contributions. First, a component-decoupled architecture in which robot backends, inference strategies, and transport middlewares form an orthogonal grid: adding a robot or a strategy touches only its own layer. Second, inspectable execution through Debug, Collect, and Eval workflows, with modes ranging from open-loop simulation to continuous real-time control. Third, every evaluation run doubles as a data collection, recording full rollouts in training-ready format alongside exhaustive logs and a side-by-side comparison viewer, so each evaluation feeds the next round of training rather than ending as an unrecorded impression. EVA-Client further consolidates major real-time inference strategies, synchronous and asynchronous execution, ACT-style temporal ensembling, Real-Time Chunking, and a naive-async ablation baseline, behind a single configuration surface.