Does Evo-1 run on UFACTORY xArm?
Capabilities
| Capability | State | Notes | Evidence |
|---|---|---|---|
| Train / fine-tune | works | ev-evo1-paper | |
| Real-robot inference | works | ev-evo1-paper, ev-evo1-xarm6-client | |
| Simulation inference | no verified evidence | — | |
| Teleop + record | no verified evidence | — |
Caveats
- Evidence covers the xArm6 only, driven by Evo-1's own websocket client script (official xArmAPI), not by the LeRobot xArm plugin; no xArm checkpoint is published.
Evidence 2 sources; the cell shows the highest tier
- T1 · upstream documentedpapersource 2025-11-06verified 2026-10-24 (1d ago)
The Evo-1 paper's real-world experiments train and evaluate Evo-1 on a 6-DoF xArm6 (wrist + fixed camera) across four manipulation tasks.
“To evaluate the model’s performance in diverse real-world scenarios, we conduct experiments using a 6-DoF xArm6 robotic arm equipped with a parallel gripper, and design four manipulation tasks involving diverse object manipulation and real-time interaction”
https://arxiv.org/abs/2511.04555 - T1 · upstream documentedsource-codesource 2026-08-18verified 2026-10-24 (1d ago)
The official Evo-1 repo ships an xArm6 inference client (Evo_1/scripts/Evo1_client_xarm6.py, using xarm.wrapper.XArmAPI).
“We provide an example of inference client script [Evo1_client_xarm6](Evo_1/scripts/Evo1_client_xarm6.py) for xArm6.”
https://github.com/MINT-SJTU/Evo-1/blob/5fd14b015013c4fd0aacf5f8f48f868ca9b870a2/README.md
Evo-1 inference speed by hardware robot-independent · inference cost only
| Compute | Runtime | Precision | Cams | p50 / p95 ms | Inference | Platform | Source |
|---|---|---|---|---|---|---|---|
| Jetson Orin Nano 8GB (Super) | ONNX (split) | fp16 | 1 | 289.18 / 292.12 | 3.45 Hz | jetpack 7.2 (L4T R39.2.x, per the repo host-setup doc at this commit) · power_mode MAXN_SUPER, pinned clocks · cuda 13.2 · tensorrt 10.16.2 (system, via ONNX Runtime TensorRT EP)conditionsboard: Jetson Orin Nano Super 8 GB developer kit · os: Ubuntu 24.04 · onnxruntime-gpu: 1.24 Inference cost only; not task success. Deterministic synthetic observations, pinned MAXN_SUPER clocks. Untrained action head: measures inference cost of the EVO1 architecture only; the author marks it as never to control a robot. Author labels the precision "mixed FP16". | T3 · community reported |
| Jetson Orin Nano 8GB (Super) | TensorRT | fp16 | 2 @640x480 | 332.77 / 333.63 | 3 Hz | jetpack 7.2.1 · power_mode MAXN_SUPER, clocks pinned with jetson_clocks (GPU 1020 MHz, CPU 1728 MHz) · tensorrt 10.16.2.10conditionsboard: Jetson Orin Nano Engineering Reference Developer Kit Super (8 GB) · l4t: R39.2.1 · os: Ubuntu 24.04.4 LTS · python: 3.12.3 · desktop: running Inference cost only; not task success. 300 s run on synthetic 640x480 observations resized by the exported policy to 448x448; 32 denoising steps; latency is runtime-only over a pre-materialized observation ring. Whole-board RAM in use 2.76 GB (unified memory, desktop running). Actions were gated against the stock policy (cosine >= 0.99999) before timing. | T3 · community reported |
| Jetson Orin Nano 8GB (Super) | ONNX (split) | fp16 | 2 | 414.67 / 424.72 | 2.41 Hz | jetpack 7.2 (L4T R39.2.x, per the repo host-setup doc at this commit) · power_mode MAXN_SUPER, pinned clocks · cuda 13.2 · tensorrt 10.16.2 (system, via ONNX Runtime TensorRT EP)conditionsboard: Jetson Orin Nano Super 8 GB developer kit · os: Ubuntu 24.04 · onnxruntime-gpu: 1.24 Inference cost only; not task success. Deterministic synthetic observations, pinned MAXN_SUPER clocks. Author labels the precision "mixed FP16". System RAM 6001 MB in use. | T3 · community reported |
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- 2026-10-25 fecccd1 Initial release: robot policy compatibility matrix · Danilo Znamerovszkij