Plan a First LeRobot Project with Clear Success and Stop Criteria

Write an observable task contract

Define the first deliverable as a decision you can make after an experiment. For example, ask whether a learned policy can transfer a particular lightweight object between specified areas under a controlled presentation procedure. Write what counts as completion, what invalidates a trial, and what the operator may change between attempts. Avoid a goal such as making the robot useful: it leaves every failure open to reinterpretation and every attractive video open to overstatement.

The LeRobot imitation-learning tutorial connects demonstration collection, training, and evaluation. Use that workflow as context for an editorial project contract: describe the permitted starting conditions, the evidence to retain, and the next decision. A trial requiring an unplanned human nudge should remain distinguishable from an unassisted completion. Choose an evaluation rule that someone outside the project can apply from the saved evidence, including borderline outcomes.

参考资料： Imitation Learning on Real-World Robots, main documentation

Freeze the software envelope before debugging behavior

This guide uses LeRobot main documentation checked on September 5, 2026, rather than claiming a universal command sequence. The installation page distinguishes source installation from packaged releases and describes optional dependencies. Before starting, record the actual package version or repository commit, operating system, environment specification, and selected hardware backend. Open documentation matching that checkout when resolving arguments or imports; a tutorial copied from a different revision is another experimental variable.

Create a project manifest that connects the environment to the task contract and stored results. Include the training configuration, dataset revision, model checkpoint identifier, and any local code changes. Give one person responsibility for accepting dependency updates during a run. When an update becomes necessary, retain the previous environment description and explain which earlier results remain comparable. Reproducibility is much easier when the team can identify the exact configuration that produced an outcome.

参考资料： LeRobot Installation, main documentation · LeRobot official repository

Choose the smallest useful workflow

Decide whether the immediate unknown concerns software, demonstrations, or physical behavior. If it concerns data loading or training configuration, begin with an appropriate existing dataset or a supported simulated workflow. If it concerns whether the target object can be observed and manipulated, budget a qualified physical feasibility assessment before promising a learned solution. These are different project stages, and each deserves its own completion criterion.

For a physical setup, require a reviewed plan for mounting, tooling, access, stopping, and reset before motion. The SO-101 documentation provides a robot-specific assembly and setup route; it is not evidence that every task or installation is suitable. Record who owns mechanical and electrical questions. A low-cost learning platform still introduces moving parts and stored energy. Do not make the policy responsible for safeguarding people, and do not use a research milestone as permission to enter production.

参考资料： SO-101 setup, main documentation

Prove observations before collecting a large dataset

Before a collection session, write an observation inventory. Name the views, describe what each view must reveal, and list the state and action meanings expected by the learning workflow. During an approved pilot, inspect an approach, interaction, and completion from the recorded evidence. Can a reviewer distinguish a missed grasp from an object hidden behind the gripper? If the distinction is unavailable, collecting more episodes may reproduce the same ambiguity.

LeRobotDataset v3.0 stores video, tabular signals, and metadata that connects episodes to shared files. That storage design motivates an acceptance check on the complete recorded dataset rather than on a camera preview alone. Open it through the intended loader, inspect episode boundaries, and confirm the expected observations are present. Keep a short accepted sample as a reference for future sessions. Its purpose is to catch configuration changes before they become an expensive collection problem.

参考资料： LeRobotDataset v3.0, main documentation

Allocate collection and review work separately

Plan demonstrations as a small research operation with named roles: operator, reviewer, and experiment owner. These roles can belong to the same person, but the work still consumes separate time. Estimate setup, approved resets, recording, episode review, annotation, and file verification independently. A calendar containing only robot motion time obscures the activities most likely to delay the next training run.

Use a collection sheet with intended variation, actual condition, outcome, and reason for rejection. Specify a narrow initial range, then reserve different conditions for evaluation. Do not quietly add unusual objects or camera arrangements halfway through a session and keep the old dataset description. The tutorial supplies the collection-to-training sequence; this allocation framework is an editorial recommendation for making that sequence reviewable. Expand collection after inspecting what the current model cannot do, not simply because storage remains available.

参考资料： Imitation Learning on Real-World Robots, main documentation

Worked hypothetical example: a tray transfer experiment

Suppose a private lab wants to compare demonstrations for moving a foam block into a tray. This is an illustrative plan, not a hardware recommendation or reported test. The first gate is documentary: the team agrees on the workspace and operating procedure. The next gate is recording quality: a pilot must contain interpretable observations and a documented outcome. Only then does the team commit to a larger collection session and a baseline training run.

The final gate compares the baseline against a fixed evaluation sheet. Record successes, incomplete attempts, interventions, and invalid trials separately. If failure clusters around one starting area, the next experiment can investigate visibility or demonstration coverage there. If the robot behaves inconsistently even during the approved demonstration workflow, return to integration review. Training a different policy is not automatically the most informative next action. This example makes spending contingent on resolving a particular uncertainty.

参考资料： Imitation Learning on Real-World Robots, main documentation · SO-101 setup, main documentation

Design evaluation before selecting a favorite checkpoint

Write an evaluation protocol before comparing models. Specify starting conditions, trial termination, acceptable assistance, and the person judging the outcome. Reserve evaluation conditions so that repeatedly watching them does not become an informal extension of training. When conditions change, create a new evaluation series instead of quietly merging results. Record all attempted trials, including those that make the project look less polished.

Choose the baseline for interpretability and compatibility with your available data and compute. Keep model selection and final evaluation conceptually separate: selecting a checkpoint on a set of trials makes those trials part of development. A smaller, honestly bounded result is more useful than a broader claim built from selected clips. LeRobot's tutorial includes policy evaluation, but the acceptance threshold belongs to your project contract and cannot be borrowed from an unrelated demonstration video.

参考资料： Imitation Learning on Real-World Robots, main documentation

End with a reproducible decision

Package the outcome as an experiment record: question, frozen setup, dataset identity, training configuration, evaluation protocol, results, and unresolved failures. Include the reason for continuing, changing scope, or stopping. Preserve unsuccessful results when they explain a decision, especially if they reveal an observation gap or an integration constraint. The next participant should not need to reconstruct the reasoning from private chat messages.

For this private forum project, decide explicitly where recordings may be stored and who may inspect them. The checked recording tutorial includes Hub upload behavior, so review the selected revision's upload configuration before collection. Keep credentials outside shared experiment notes. Finish with a realistic handoff: another person should be able to find the artifacts and understand the evidence, while physical operation remains subject to the approved local procedure and qualified supervision.

参考资料： Imitation Learning on Real-World Robots, main documentation · LeRobot official repository

检查清单

• Write observable success, intervention, and invalid-trial definitions.
• Record the LeRobot version or commit and the full experiment configuration.
• Assign qualified responsibility for physical integration and operating procedures.
• Review a pilot dataset through the intended loader before scaling collection.
• Reserve evaluation conditions and record every attempted trial.
• Budget review and reset time as well as recording and training time.
• Confirm storage access and upload settings before collecting private material.

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