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arXiv · 2607.02646

EVA-Client: A Unified Data Collection, Inference, and Deployment Framework for Embodied Policies on Real Robots

Heqing Yang·Yang Yi·Liyao Wang·Linqing Zhong·Donglin Yang·Ruipu Wu·Zitong Bai·Fengjiao Chen·Manyuan Zhang·Linjiang Huang·Si Liu
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Abstract

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.

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Heqing Yang, Yang Yi, Liyao Wang, Linqing Zhong, Donglin Yang, Ruipu Wu, Zitong Bai, Fengjiao Chen, Manyuan Zhang, Linjiang Huang, Si Liu. 2026-07-02. EVA-Client: A Unified Data Collection, Inference, and Deployment Framework for Embodied Policies on Real Robots. https://arxiv.org/abs/2607.02646

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