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Does SmolVLA run on Trossen WidowX AI?

works with caveats
T1 · upstream documentedlast verified 2026-10-24 · 1d agoLeRobot

Capabilities

CapabilityStateNotesEvidence
Train / fine-tuneworksev-trossen-smolvla-ckpt
Real-robot inferenceworksev-trossen-plugin-readme, ev-trossen-docs-async
Simulation inferenceno verified evidence—
Teleop + recordno verified evidence—

Caveats

Evidence 3 sources; the cell shows the highest tier

  1. T1 · upstream documentedvendor-docssource 2026-09-16verified 2026-10-24 (1d ago)

    Official Trossen plugin README names SmolVLA among the VLA policies that run on WidowX AI robots via lerobot-record or async inference, given the extra dependencies.

    “**VLA policies (π₀, π₀.₅, SmolVLA) need extra dependencies** (transformers/peft) that the base install omits”
    https://github.com/TrossenRobotics/lerobot_trossen/blob/337ed74df1ee617ddfb5987a9f7464cf3089c4a2/README.md
  2. T1 · upstream documentedvendor-docsverified 2026-10-24 (1d ago)

    Trossen async-inference tutorial for Trossen AI kits covers serving SmolVLA (swap the pi extra for smolvla).

    “smolvla: required for SmolVLA policies.”
    https://docs.trossenrobotics.com/trossen_arm/main/tutorials/lerobot_plugin/async_inference.html
  3. T3 · community reportedhub-checkpointsource 2025-07-31verified 2026-10-24 (1d ago)

    config.json type=smolvla (base lerobot/smolvla_base); train_config dataset TrossenRoboticsCommunity/bimanual-widowxai-handover-cube_01, whose meta/info.json robot_type=bi_widowxai_follower. Trossen also publishes SmolVLA checkpoints on trossen_ai_stationary datasets.

    https://huggingface.co/TrossenRoboticsCommunity/smolvla-handover-cube-widowxai

SmolVLA inference speed by hardware robot-independent · inference cost only

ComputeRuntimePrecisionCamsp50 / p95 msInferencePlatformSource
Jetson Orin Nano 8GB (Super)TensorRTfp162 @640x480107.55 / 108.489.3 Hzjetpack 7.2.1 · power_mode MAXN_SUPER, clocks pinned with jetson_clocks (GPU 1020 MHz, CPU 1728 MHz) · tensorrt 10.16.2.10
conditions

board: 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)fp162189.89 / 190.935.25 Hzjetpack 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)
conditions

board: 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 eagerfp3221167.93 / 1176.650.86 Hzjetpack 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 0.5.1
conditions

board: 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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SmolVLA on Trossen WidowX AI: works with caveats

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History

  1. 2026-10-25 fecccd1 Initial release: robot policy compatibility matrix · Danilo Znamerovszkij