机器人训练终于有了闭环工具箱
训练机器人策略最烦人的不是算法，而是每次跑完实验数据就丢了，下次还得从头来。EVA-Client 把部署、数据采集、评估塞进同一个框架，关键设计是：每次评估跑完，数据自动存成训练格式，还带日志和对比回放，等于每次实验都在为下一轮训练攒弹药。它还把机器人驱动、推理策略、通信中间件拆成独立层，换机器人或换策略只改对应层，不用重写整个系统。做机器人实操的研究者，这是你明天就能装上的那种。
📄 原文摘要(英文)
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.