One policy, any robot — the smooth all-in-one real-robot stack. Debug, record, evaluate, visualize, all in the browser.

EVA-Client driving an AgileX bimanual arm end-to-end from the browser — teleop → record → π₀ checkpoint → smooth async deploy. Real hardware, not a rendering.

Jump to:  What's NEW · What you get · Robot zoo · Protocols · Architecture · Documentation · Roadmap · Cite

• [2026-07-22] New RL Workspace. EVA-Client now supports human-in-the-loop (HIL) intervention during policy rollouts and real-time value-curve visualization.

• [2026-07-18] Headless CLI + ZMQ control channel. Drive the full deploy / eval / RL / collect loop from a single .py config without the browser — every console button (incl. HIL takeover) mirrored on a ZMQ socket. See docs/console.md and docs/control-channel.md.
• [2026-07-03] EVA-Client is open-sourced!
• [2026-07-03] Paper, docs, and project page are live! Read the Technical Report, browse the Documentation (中文), and visit the Project Page.

• 🚀 Deployment. One command brings up a real-robot closed loop: .py config → transport (ROS1 / ROS2 / ZeroMQ / offline dataset) → policy backend (OpenPI, OpenPI-RTC, StarVLA, GR00T, mock, replay) → inference strategy (sync / async / naive / ACT-ensemble / RTC) with live latency compensation. 6 robots already — joint-space or EEF-space (PyRoki IK), all live-switchable from the DEBUG tab.
• 📊 Evaluation. Multi-checkpoint sweeps with per-trial records: every rollout captures camera video, 3D URDF scene, per-dimension state charts, and per-prompt milestone scores into dataset metadata, then replays synchronously in the RESULT tab. Prompt shuffling, and remote policy servers over SSH port-forward are first-class.
• 🎥 Data collection. Teleop capture straight into LeRobot v2.1 episodes from the COLLECT tab — background saver, in-tab QC PASS/FAIL replay, camera streams encoded to mp4, per-frame green/red quality flags. Teleop demos and model rollouts share one on-disk layout.

✅ Supported  ·  🚧 In development  ·  📦 To be added

Full guides live in docs/. Start here:

• More robots. Extend the robot zoo to more embodiments — dual-arm manipulators (YAM, Tianji, …), humanoids (Unitree H1/G1, Fourier GR-1, Booster T1, …) and mobile / wheeled platforms (mobile ALOHA, Galaxea R1 base, quadruped + arm).
• Human-in-the-loop data collection for RL. Interventions during policy rollout captured as preference / correction data, DAgger-style relabeling, and reward-model signals piped back through the LeRobot episode format for online RL fine-tuning.
• Embodied agent. Wrap the deployment + evaluation loop with a language-driven planner (VLM / VLA + tool use) so long-horizon tasks can be decomposed, executed, verified, and re-planned end-to-end from the same console.
• Data annotation. Extend Collect mode with fine-grained task and sub-task annotation, segmenting long-horizon episodes into labeled sub-task units and milestones within the same LeRobot dataset. This makes a collection reusable at the level of individual manipulation phases.

If EVA-Client is useful for your research or product, please cite:

@misc{yang2026evaclient,
      title={EVA-Client: A Unified Data Collection, Inference, and Deployment Framework for Embodied Policies on Real Robots},
      author={Heqing Yang and Yang Yi and Liyao Wang and Linqing Zhong and Donglin Yang and Ruipu Wu and Zitong Bai and Fengjiao Chen and Manyuan Zhang and Linjiang Huang and Si Liu},
      year={2026},
      eprint={2607.02646},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2607.02646},
}

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