A robot benchmark result is a measurement attached to a protocol, checkpoint and environment. Read those fields before the score, and compare only results that answer the same evaluation question.
Identity boundary. Original PhysicalAI.best editorial guide. Source links support factual examples; evaluation questions and decision rules are editorial guidance, not upstream endorsements.
Write the result identity first
Capture the benchmark and release, task suite, train/test split, model checkpoint, embodiment, inputs and evaluation date. Include the code revision when available. A model-family name does not identify the tested system: OpenVLA-OFT, for example, is a distinct implementation from its base family. Without these fields, a score may be accurate but unusable for a fair comparison.
Lifelong-Robot-Learning · Publication date not disclosed · Source accessed 2026-09-28
Official README inspected live. Repository code terms do not automatically cover model weights, data, or third-party assets.
Read the unit and success definition
LIBERO reports task success within defined suites; CALVIN evaluates chains of language-conditioned tasks. These metrics do not express the same outcome. Inspect how an episode begins and ends, whether intermediate tasks share state and what event counts as success. Do not normalize unrelated scores into a global ranking. Present separate results with their original metric and explain the decision each can inform.
mees · Publication date not disclosed · Source accessed 2026-09-28
Official README inspected live. Repository code terms do not automatically cover model weights, data, or third-party assets.
Inspect evaluation conditions and assistance
Check the trial count, seeds, resets, initialization, camera inputs, action limits and checkpoint-selection procedure when disclosed. Distinguish zero-shot evaluation from local adaptation. Record whether human intervention is permitted and how failed trials are handled. Missing details should remain explicit evidence gaps rather than being filled with standard-looking defaults. Ask whether another team could reproduce the measurement from the published record.
mees · Publication date not disclosed · Source accessed 2026-09-28
Official README inspected live. Repository code terms do not automatically cover model weights, data, or third-party assets.
Keep simulation and physical tests visible
SimplerEnv offers defined simulation protocols intended to study robot policies in settings related to physical evaluation. It still produces simulation results. A useful record states the environment plainly and describes any reported connection to real-robot behavior. Do not turn correlation or visual similarity into equivalence with a field deployment. Robot hardware, sensing and task conditions belong beside real-robot results too.
SIMPLER research team · Publication date not disclosed · Source accessed 2026-09-28
Check provenance and reuse before publishing
Prefer the original paper, repository or maintained leaderboard, and distinguish an author-reported result from an independent reproduction. Inspect the license for code, weights, benchmark assets and the result collection separately. Link to the source and publish only the data permitted for the intended use. For a buying decision, use the result to select follow-up tests rather than declaring an unconditional winner.
Related reading is an editorial crosslink. Sourced connections describe relationships reported in the cited material. A link to a versioned profile does not establish compatibility with that version unless the connection note explicitly identifies it.
Record history & verified changes
A research review records when we checked a source. It does not mark a product launch or a new deployment.
Initial reviewed record. No subsequent field change has been recorded.