Episode
EVA-Client: A Unified Data Collection, Inference, and Deployment Framework for Embodied Policies on Real Robots
- Podcast
- Daily Paper Cast
- Published
- Jul 8, 2026
- Duration seconds
- 1350
- Processing state
not_requested- Canonical source
- https://share.transistor.fm/s/ad2d40dc
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Summary
š¤ Upvotes: 23 | cs.RO, cs.CV Authors: Heqing Yang, Yang Yi, Liyao Wang, Linqing Zhong, Donglin Yang, Ruipu Wu, Zitong Bai, Fengjiao Chen, Manyuan Zhang, Linjiang Huang, Si Liu Title: EVA-Client: A Unified Data Collection, Inference, and Deployment Framework for Embodied Policies on Real Robots Arxiv: http://arxiv.org/abs/2607.02646v1 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.