Does SmolVLA run on OpenArm?
Capabilities
| Capability | State | Notes | Evidence |
|---|---|---|---|
| Train / fine-tune | works | ev-hub-gtgando-smolvla-openarm, ev-hub-tetraxcode-smolvla-openarm | |
| Real-robot inference | works | Documented on the bimanual OpenArm (--robot.type=bi_openarm_follower, three OpenCV cameras) with OpenArm Mini leaders, running the policy under lerobot-rollout --strategy.type=dagger with RTC inference (--inference.type=rtc); human interventions are recorded for fine-tuning. | ev-lerobot-hil-openarm-default, ev-lerobot-hil-openarm-mode |
| Simulation inference | no verified evidence | — | |
| Teleop + record | no verified evidence | — |
Evidence 4 sources; the cell shows the highest tier
- T1 · upstream documentedofficial-docssource 2026-08-03verified 2026-10-24 (1d ago)lerobot 0.6.1
LeRobot's human-in-the-loop data collection guide runs trained policies on the bimanual OpenArm follower by default.
“> The provided commands default to `bi_openarm_follower` + `bi_openarm_mini`.”
https://github.com/huggingface/lerobot/blob/v0.6.1/docs/source/hil_data_collection.mdx - T1 · upstream documentedofficial-docssource 2026-08-03verified 2026-10-24 (1d ago)lerobot 0.6.1
The guide names this policy for the lerobot-rollout dagger command on bi_openarm_follower (RTC inference (--inference.type=rtc)).
“**With RTC for large models (Pi0, Pi0.5, SmolVLA):**”
https://github.com/huggingface/lerobot/blob/v0.6.1/docs/source/hil_data_collection.mdx - T3 · community reportedhub-checkpointverified 2026-10-24 (1d ago)
config.json type=smolvla; train_config.json dataset gtgando/openarm-pick-place-50ep whose meta/info.json robot_type=openarm_follower (100 episodes).
https://huggingface.co/gtgando/openarm-smolvla-pick-place-40k - T3 · community reportedhub-checkpointverified 2026-10-24 (1d ago)
config.json type=smolvla; train_config.json dataset TetraxCode/openarm_pick_place_v1 whose meta/info.json robot_type=openarm_bimanual (19 episodes); fine-tuned from lerobot/smolvla_base.
https://huggingface.co/TetraxCode/my_smolvla_openarm
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 |
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- 2026-10-25 fecccd1 Initial release: robot policy compatibility matrix · Danilo Znamerovszkij