Does SmolVLA run on SO-101?
Capabilities
| Capability | State | Notes | Evidence |
|---|---|---|---|
| Train / fine-tune | works | ev-smolvla-paper-so101 | |
| Real-robot inference | works | ev-lerobot-smolvla-doc, ev-smolvla-paper-so101 | |
| Simulation inference | no verified evidence | — | |
| Teleop + record | works | ev-smolvla-paper-datasets |
Evidence 3 sources; the cell shows the highest tier
- T1 · upstream documentedofficial-docssource 2026-08-03verified 2026-10-24 (1d ago)lerobot 0.6.1
LeRobot v0.6.1 SmolVLA guide: fine-tune smolvla_base, then run the fine-tuned model on the real robot with lerobot-rollout --robot.type=so101_follower; its figure shows real-world SO101 results.
“lerobot-rollout \ --strategy.type=base \ --robot.type=so101_follower \”
https://github.com/huggingface/lerobot/blob/v0.6.1/docs/source/smolvla.mdx - T1 · upstream documentedpapersource 2025-06-02verified 2026-10-24 (1d ago)
SmolVLA paper, Table 4: real-world SO101 Pick-Place-Lego benchmark, ACT vs SmolVLA, in- and out-of-distribution.
“Similarly, on SO101 (see Table 4), SmolVLA surpasses ACT in both in-distribution and out-of-distribution (OOD) settings.”
https://arxiv.org/html/2506.01844v1 - T1 · upstream documentedpapersource 2025-06-02verified 2026-10-24 (1d ago)
SmolVLA paper: real-world evaluation datasets were recorded on SO-100 (three tasks) and SO-101 (one task).
“For real-world evaluation, we collected three datasets using the SO-100 robot arm”
https://arxiv.org/html/2506.01844v1
SmolVLA inference speed by hardware robot-independent · inference cost only
| Compute | Runtime | Precision | Cams | p50 / p95 ms | Inference | Platform | Source |
|---|---|---|---|---|---|---|---|
| Jetson Orin Nano 8GB (Super) | TensorRT | fp16 | 2 @640x480 | 107.55 / 108.48 | 9.3 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 512x512; 10 denoising steps; latency is runtime-only over a pre-materialized observation ring. Whole-board RAM in use 1.78 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 | 189.89 / 190.93 | 5.25 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. Configured EP priority 7 graphs TensorRT / 2 CPU. System RAM 2385 MB in use. | T3 · community reported |
| Jetson Orin Nano 8GB (Super) | PyTorch eager | fp32 | 2 | 1167.93 / 1176.65 | 0.86 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 · lerobot 0.5.1conditionsboard: Jetson Orin Nano Super 8 GB developer kit · os: Ubuntu 24.04 Inference cost only; not task success. Deterministic synthetic observations, pinned MAXN_SUPER clocks. Stock LeRobot SmolVLAPolicy in PyTorch FP32 (LeRobot 0.5.1 env, JetPack-matched torch). RSS peak 3826 MB, system RAM 4126 MB in use. | T3 · community reported |
Embed this cell
Badge for READMEs and model cards. It always shows the current state.
[](https://caniembody.pages.dev/compat/so101/smolvla)<iframe src="https://caniembody.pages.dev/embed/so101/smolvla" width="380" height="132" style="border:0" title="SmolVLA on SO-101 | CanIEmbody"></iframe>History
- 2026-10-25 fecccd1 Initial release: robot policy compatibility matrix · Danilo Znamerovszkij