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02 — Data · AI Model Performance

OpenVLA-OFT

Open weightsStanford

2 of 6 axes published · values checked against their primary sources on 23 Oct 2026

  1. Success

    Does it do the trained task? (% of trials, trained setup)

    13%

    Real robotGrade B · Paper or report with method (rubric or error bars)200 trialsALOHA · 4 tasks

    Independent test: clean dish 29%, sponge 23%, unzip 0%, fold 0%. Its own paper reports 97.1% on LIBERO (simulation).

    Source: Benchmarking VLAs on ALOHA, arXiv 2511.11298 · Table 3, macro-average

  2. Robustness

    Does it survive the cell drifting? (% under the perturbation set)

    6%

    Real robotGrade B · Paper or report with method (rubric or error bars)200 trialsALOHA · unseen objects

    Unseen objects at random positions: 13%, 11%, 0%, 0%.

    Source: Benchmarking VLAs on ALOHA, arXiv 2511.11298 · Table 5 (mean of 4 tasks)

  3. Instruction

    Does rewording the instruction break it? (% under paraphrase)

    Owed

    Not reported.

  4. Recovery

    Does it fix its own mistakes? (% of injected failures recovered)

    Owed

    Not reported.

  5. Speed

    Is it as fast as a trained person? (human cycle time ÷ robot cycle time)

    Owed

    Not reported.

  6. Interventions

    How often does a person step in? (per 1,000 cycles)

    Owed

    Not reported.

Built on OpenVLA-OFT

Published work that starts from OpenVLA-OFT: fine-tunes, add-ons and models trained from it. Their results are listed here, apart from OpenVLA-OFT's own values above, with the source each one comes from.

  1. QbD-VLA

    Inference settingOpen code

    Colorado School of Mines · Northeastern University · USC Institute for Creative Technologies · University of Central Florida · 28 Oct 2026

    Shrinks the model by storing most of its numbers at low precision, choosing which parts to compress by testing the robot’s task success in simulation.

    • Success91.8%−4.7 pts vs OpenVLA-OFT in the same paper (96.5%)Simulation · LIBERO, 4 suites, 4-bit weights · 800 trials · Grade BReleased per-suite checkpoints. Biggest drop on LIBERO-Long: 80.5% vs 92.5%.

    Source: QbD-VLA paper, arXiv 2610.38661

  2. TRIAGE

    Inference setting

    Seoul National University · CMU · 25 Oct 2026

    No retraining: skips or reuses parts of each camera frame to run faster on onboard computers.

    • Success95.8%−1.3 pts vs OpenVLA-OFT in the same paper (97.1%)Simulation · LIBERO average · Grade BTrades 1.3 points for speed: 443 vs 653 ms per call on a Jetson AGX Orin.

    Source: TRIAGE paper, arXiv 2610.34519

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