fieldtrial

Find out whether your robot policy actually got better.

fieldtrial is an open-source Python framework for statistically rigorous, real-world evaluation of robot policies. LeRobot trains the policy; fieldtrial tells you whether it actually got better.

Why

Real-world evaluation is slow, noisy and ad hoc. With 40 rollouts, the 95% confidence interval for a success rate is 12 to 15 percentage points wide on each side, so many apparent improvements from a fine-tuning run are just noise. fieldtrial helps at every stage:

• Before: how many rollouts do I need? What is the smallest difference this evaluation can detect?
• During: a randomized, blinded schedule, and a phone-friendly operator console that records the outcome, the furthest stage reached and the failure mode of each rollout.
• After: the right statistics for the design (exact tests, paired analyses, multiplicity control), honest wording, and a self-contained report.

30-second demo

$ uvx fieldtrial compare 74/80 91/120
Arm 1: 74/80 = 92.5% (95% CI 84.6%–96.5%)
Arm 2: 91/120 = 75.8% (95% CI 67.4%–82.6%)
Difference +16.7 pp, Newcombe 95% CI [+6.2 pp, +26.0 pp]
Boschloo p = 0.0020 (two-sided); rejects H0 at α = 0.05
Fisher p = 0.0022

$ uvx fieldtrial power --p1 0.76 --p2 0.90      # rollouts needed to detect 76% → 90%
112 per arm (pooled-z)

$ uvx fieldtrial demo                           # a simulated study in the console

fieldtrial demo opens a half-run, blinded study with a simulated robot. Run the rest from the keyboard (Space, Space, Enter), unblind, and read the report.

Quickstart

$ uv tool install fieldtrial              # or: pip install fieldtrial
$ fieldtrial init my-study                # writes my-study/study.yaml
$ fieldtrial plan my-study --baseline 0.75
$ fieldtrial lock my-study                # freezes the design and randomizes the schedule
$ fieldtrial serve my-study --lan         # scan the QR code with a phone at the robot
$ fieldtrial unblind my-study
$ fieldtrial report my-study              # my-study/reports/report.html

A study is one folder: study.yaml (the design) and a SQLite database. Everything is local and works offline; fieldtrial sends no telemetry.

• Documentation: quickstart, concepts, guides and a statistics reference.
• Re-analysis of Dream Machines' published pi0.5 results: which of 30 published comparisons the data actually resolve.

What you get

• Design: randomized complete blocks with balanced arm order, blind codes, a design hash, locking and logged amendments.
• Console: sessions with a rig checklist, a timer, stage and failure-tag labels, 10-second undo, invalid trials with automatic rescheduling, a live mirror screen, keyboard and foot-pedal keys, LAN access with a QR code.
• Analysis: the primary test follows from the locked design (exact McNemar with a Tango interval, Cochran–Mantel–Haenszel, or Cochran's Q with Holm), plus an independent-samples sensitivity analysis, stage funnels, time to success, drift checks and a list of every deviation from the plan.
• Reports: self-contained HTML with charts, Markdown for pull requests, and a versioned JSON results model.
• Integration: a REST API with a dependency-free Python client for custom runtimes, CSV import and export, and fieldtrial.stats as a library.

Every statistical function is tested against an independent reference implementation. Reports only describe a difference when the pre-registered test rejects; otherwise they say what the study could have detected.

How it fits with LeRobot and openpi

fieldtrial complements LeRobot and openpi and never forks them. It is not a training framework, a simulation benchmark, a robot driver, a labeling platform or a cloud service. In manual mode, fieldtrial schedules and records the trials and you run the robot however you like. Runners that launch LeRobot rollouts and route openpi traffic for real blinding are planned for v0.2; see the roadmap.

How to cite

If fieldtrial helps your research, please cite it (see CITATION.cff):

@software{benko_fieldtrial,
  author  = {Benko, Rok},
  title   = {fieldtrial: statistically rigorous real-world evaluation for robot policies},
  url     = {https://github.com/rokbenko/fieldtrial},
  license = {Apache-2.0}
}

For evaluation practice in general, see Kress-Gazit et al. (2024), Robot Learning as an Empirical Science: Best Practices for Policy Evaluation, arXiv:2409.09491.

Development

See CONTRIBUTING.md.