Physical AI Lab // Episode 0.6 // One-pager
How to judge robot news?
Three levels of evidence, then four more questions
Three key points
- First check who said it: O official and first-hand, R paper or preprint, M media, second-hand. Discount what makers say about themselves; check second-hand reports against the original.
- Then ask four questions: demo or deployment? Success on what task? Peak or usual (up to = peak)? Is there a dated, first-hand source?
- Check facts, metrics and conditions before adjectives. Undisclosed output or success rates stay undisclosed. Check reporting period, publication date and current status separately. The Tesla case cites Q2 2026 page 6, not today's progress.
Evidence: O official · R research · M media
| M | 2030 | is when Daifuku plans a field trial of unmanned warehouses, according to Japanese media (M, second-hand; a demo / future plan for 2030, not a current deployment) |
| R | Up to 70% | less work evaluating robot policies than standard batch methods, one preprint says (up to = peak; it's effort saved on evaluation, not a task success rate) |
| O | Construction · Q2 2026 | First-gen Optimus lines being installed; table lists Construction. A maker's report for that period, not proof of output, deliveries or reliable work. |
Further reading
- Physical AI Frontier Radar (every item marked O/R/M, with original links)
https://noteflowai.github.io/physical-ai-radar/ - Radar snapshot · 2026-09-28 (the items used in this episode's practice)
https://raw.githubusercontent.com/noteflowai/physical-ai-radar/main/radar/daily/2026-09-28.zh.md - Beyond Binary Success (arXiv 2603.13616, the original "up to 70%")
https://arxiv.org/abs/2603.13616 - Tesla · Q2 2026 Update · p. 6 (reporting period ends 2026-06-30)
https://assets-ir.tesla.com/tesla-contents/IR/TSLA-Q2-2026-Update.pdf