Physical Intelligence's π0.7 shows compositional generalization to untrained robot tasks
Physical Intelligence published π0.7 on 2026-04-16, a steerable robot foundation model that combines skills to do tasks it was never trained on (e.g. operating an air fryer) and can be coached in plain language — lifting air-fryer success from ~5% to ~95% in half an hour of prompting; the startup was reported to be raising ~$1B at an ~$11B valuation.
Key facts
- Release: 2026-04-16 (π blog: 'a Steerable Model with Emergent Capabilities')
- Air fryer task: ~5% -> ~95% success after ~30 min of natural-language coaching, no retraining
- Generalizes across robot embodiments
- Funding: previously $1B+ raised at $5.6B valuation; reported (Bloomberg, Mar 2026) talks to raise ~$1B at >$11B
What happened
π0.7 blends skills learned in unrelated settings; the air fryer example appeared only in two fragmentary training references. Plain-language coaching lets field operators tune behavior without retraining.
Why it matters
Emergent, promptable generalization is what would let general-purpose robots be deployed without per-task data collection.
Changelog
- 2026-09-29: created
Models
- π0.7 Physical Intelligence · current
Related events
- Figure Helix 2.5: humanoids do chores zero-shot in 30 never-seen homes ★★★★★
- Physical Intelligence's π*0.6 learns from real-world experience with RL (Recap), running tasks for hours ★★★
- Generalist GEN-1 claims 99% success on simple robot tasks, trained on 500k+ hours of human wearable data ★★★★
- Ai2 releases MolmoAct 2, a fully open robot action-reasoning model that beats π0.5 on real-world tasks ★★
Sources (3)
- officialPhysical Intelligence: π0.7
- pressTechCrunch: Physical Intelligence says its new robot brain can figure out tasks it was never taught
- pressBloomberg: robotics lab in talks at $11B valuation
id: 2026-04-16-physical-intelligence-pi-0-7 · updated 2026-09-29 · open in the interactive timeline