Does Evo-1 run on ALOHA (stationary)?
Scope: The upstream Evo-1 repo ships a community-contributed real-ALOHA inference client (ROS1 Noetic, Interbotix); no ALOHA training recipe, checkpoint or on-robot results are documented.
Capabilities
| Capability | State | Notes | Evidence |
|---|---|---|---|
| Train / fine-tune | no verified evidence | — | |
| Real-robot inference | partial | Client/server inference scripts only. | ev-evo1-readme, ev-evo1-aloha-readme |
| Simulation inference | no verified evidence | — | |
| Teleop + record | no verified evidence | — |
Evidence 2 sources; the cell shows the highest tier
- T1 · upstream documentedofficial-docssource 2025-12-15verified 2026-10-24 (1d ago)
Evo-1 README news entry (2025-12-15) announcing ALOHA dual-arm inference code contributed by a community user.
“Added Evo-1 inference code in Aloha dual arm (Implemented by community user @meijie-jesse)”
https://github.com/MINT-SJTU/Evo-1/blob/5fd14b015013c4fd0aacf5f8f48f868ca9b870a2/README.md - T1 · upstream documentedsource-codeverified 2026-10-24 (1d ago)
Run instructions for Evo1_server.py + Evo1_client_aloha.py on a real ALOHA built per tonyzhaozh/aloha (robot_utils.py uses interbotix_xs_msgs).
“This example demonstrates how to run EVO1 with a real robot using an ALOHA setup.”
https://github.com/MINT-SJTU/Evo-1/blob/5fd14b015013c4fd0aacf5f8f48f868ca9b870a2/Evo_1/scripts/README_Aloha.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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[](https://caniembody.pages.dev/compat/aloha/evo1)<iframe src="https://caniembody.pages.dev/embed/aloha/evo1" width="380" height="132" style="border:0" title="Evo-1 on ALOHA (stationary) | CanIEmbody"></iframe>History
- 2026-10-25 fecccd1 Initial release: robot policy compatibility matrix · Danilo Znamerovszkij