Does Evo-1 run on SO-100?
Capabilities
| Capability | State | Notes | Evidence |
|---|---|---|---|
| Train / fine-tune | works | Also trainable through LeRobot's native evo1 policy (community checkpoint). | ev-evo1-paper-so100, ev-hub-beilinghamburger-evo1-so100 |
| Real-robot inference | works | ev-evo1-paper-so100, ev-evo1-readme-so100-so101 | |
| Simulation inference | no verified evidence | — | |
| Teleop + record | no verified evidence | — |
Evidence 3 sources; the cell shows the highest tier
- T1 · upstream documentedpapersource 2025-12-05verified 2026-10-24 (1d ago)
Evo-1 paper (v2), appendix 'Deployment on LeRobot SO-100 robot': Evo-1 deployed on an SO-100 for a real-world cube pick-and-place task.
“We deploy our policy on the LeRobot SO-100 platform, a compact desktop manipulator designed for fine-grained tabletop manipulation.”
https://arxiv.org/html/2511.04555v2 - T1 · upstream documentedsource-codesource 2026-08-18verified 2026-10-24 (1d ago)
Official Evo-1 repo README: SO100/SO101 inference via the LeRobot framework (instructions on its evo1-lerobot branch).
“Added Evo-1 inference in the LeRobot framework for SO100/SO101”
https://github.com/MINT-SJTU/Evo-1/blob/5fd14b015013c4fd0aacf5f8f48f868ca9b870a2/README.md - T3 · community reportedhub-checkpointsource 2025-12-11verified 2026-10-24 (1d ago)
config.json type=evo1; train_config.json dataset.repo_id=Beilinghamburger/so100_vla_dataset, whose meta/info.json robot_type=so100_follower (100 episodes). Trained with LeRobot's native evo1 policy.
https://huggingface.co/Beilinghamburger/evo1_so100_vla
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