Does SmolVLA run on reBot Arm B601-DM?
Scope: Training on reBot B601-DM data is documented by the vendor (Seeed wiki 'B601-DM with LeRobot'). The wiki's on-robot evaluation command (lerobot-record with --policy.path and no teleoperator) is rejected by the Seeed LeRobot fork the wiki installs, which directs policy deployment to lerobot-rollout, so on-robot inference is not validly documented. Public checkpoints trained on B601-DM data also exist.
Capabilities
| Capability | State | Notes | Evidence |
|---|---|---|---|
| Train / fine-tune | works | ev-seeed-wiki-b601-dm-lerobot, ev-hub-namin72-smolvla-rebot | |
| Real-robot inference | no verified evidence | — | |
| Simulation inference | no verified evidence | — | |
| Teleop + record | no verified evidence | — |
Evidence 3 sources; the cell shows the highest tier
- T1 · upstream documentedvendor-docssource 2026-10-15verified 2026-10-25 (today)
Seeed wiki 'B601-DM with LeRobot' (Training and Evaluation section). SmolVLA tab: fine-tune lerobot/smolvla_base on your reBot dataset (lerobot-train, 20k steps), then evaluate with lerobot-record --robot.type=seeed_b601_dm_follower --policy.path=...
“Use `smolvla_base` (the pretrained 450M model) as a starting point and fine-tune on your dataset.”
https://github.com/Seeed-Studio/wiki-documents/blob/86afe5634df5713aec98cf77c09a7e8d670feb1d/sites/en/docs/Robotics/Robot_Kits/reBot_Arm/B601_DM/reBot_Arm_B601_DM_Lerobot.md - T1 · upstream documentedsource-codesource 2026-08-28verified 2026-10-25 (today)
Seeed's LeRobot fork (main, pyproject version 0.6.2), which the wiki tells users to install: RecordConfig raises an error when no teleoperator is given and points policy deployment to lerobot-rollout.
“For policy-based deployment, use lerobot-rollout instead.”
https://github.com/Seeed-Projects/lerobot/blob/4aaff99be4a1d81568c08c8f0296b41b40c99ec4/src/lerobot/scripts/lerobot_record.py - T3 · community reportedhub-checkpointverified 2026-10-24 (1d ago)
config.json type=smolvla; train_config.json dataset namin72/pick-up-green-eraser-place-in-brown-gray-baskets-rgbd whose meta/info.json robot_type=rebot_b601_follower (LeRobot-native B601-DM, 192 episodes); fine-tuned from lerobot/smolvla_base.
https://huggingface.co/namin72/smolvla-rebot-b601-green-eraser-brown-gray-100k
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