Physical Intelligence logo

Physical Intelligence

StartupSan Francisco, CA, United StatesFounded 2024

Develops general-purpose vision-language-action foundation models (the π family) intended to control many kinds of robots.

Overview

Physical Intelligence is a San Francisco-based company formed in 2024 that develops general-purpose AI models for robots. It was co-founded by researchers from Google DeepMind, UC Berkeley and Stanford together with former Stripe executive Lachy Groom. The company has disclosed a $70 million initial raise (March 2024), a $400 million round (November 2024) and a $600 million Series B led by CapitalG (November 2025) at a reported valuation of about $5.6 billion.

Sources: finance.yahoo.com (12 Mar 2024) · therobotreport.com (4 Nov 2024) · therobotreport.com (25 Nov 2025)

Type
Startup
Headquarters
San Francisco, CA, United Statestherobotreport.com, 25 Nov 2025
Founded
2024finance.yahoo.com, 12 Mar 2024
Website
pi.website

Key people

Funding history

Private company · $1.07B disclosed in USD across 3 of 3 listed rounds

Funding rounds for Physical Intelligence
DateRoundAmountLeadSource
12 Mar 2024Seed$70Mn/afinance.yahoo.com, 12 Mar 2024
4 Nov 2024Series AValuation: $2.4B$400MJeff Bezos, Thrive Capital, Lux Capitaltherobotreport.com, 4 Nov 2024
Nov 2025Series BValuation: ~$5.6B (reported)$600MCapitalGtherobotreport.com, 25 Nov 2025

Ranking

#4 of 104 model companies · score 87.4 / 100 · updated 19 Oct 2026

  • Published benchmarks 35%99%
  • Open weights 20%60%
  • Adoption 20%86%
  • Released models 15%100%
  • Disclosed funding 10%85%

Each bar shows an input from 0 to 100% before weighting. 3 of 5 listed models have open weights. Most-starred official repository: Physical-Intelligence/openpi (14.3k stars, 19 Oct 2026).

How we rank model companies

The score (0–100) measures public, checkable evidence about a company's robot models. It does not measure private capability. A company that publishes no benchmark results and no weights can still have strong models. Every input is computed from the data on each company page, and every number there links to its source.

InputWeightHow it is measured
Published benchmarks35%For each scored benchmark, the company's best result divided by the best result any company has published, averaged over the benchmarks it reports. No published results scores 0. Results come only from the model's own paper, model card or the official benchmark leaderboard. Scored: LIBERO (4-suite average success, %); SimplerEnv Google Robot, visual matching (%); SimplerEnv Google Robot, variant aggregation (%); SimplerEnv WidowX / Bridge (%); CALVIN ABC→D (average length, 0–5); RoboArena (pairwise rating).
Open weights20%Share of the company's listed models whose weights the public can download.
Adoption20%GitHub stars on the company's most-starred official model repository, on a log scale relative to the most-starred repository in the list. Hugging Face downloads are shown on company pages but not scored, because gated repositories report zero.
Released models15%Number of publicly announced, named models, capped at 5.
Disclosed funding10%The sum of the funding rounds listed on the company's profile that were disclosed in USD, on a log scale from $10M to the largest sum in the list. Rounds reported only in other currencies are listed but not converted or counted. This input applies only to private startups and robot makers. For big-tech divisions, research labs and listed companies it is n/a, and its weight is shared across the other inputs in proportion.

Sources. arXiv papers and official model cards for benchmarks and parameters; official leaderboards (RoboArena); the GitHub and Hugging Face APIs for stars and downloads; company announcements and established press for funding. Missing values are shown as n/a, never estimated.

Updated 19 Oct 2026. Benchmark protocols differ between papers, for example in fine-tuning and number of trials, so small gaps between close scores are not meaningful.

Submit or correct data →

Open-weights models (3)

Physical Intelligence logo

π0.5

Open weightsVLA

Successor to π0 co-trained on heterogeneous robot, web and high-level semantic data, aimed at open-world generalization such as cleaning unseen homes; base, LIBERO and DROID checkpoints released in openpi.

Released
22 Apr 2025
Parameters
n/a
License
Apache-2.0
Embodiments
mobile manipulator, single-arm, bimanual
GitHub stars
14.3k as of 19 Oct 2026
HF downloads (all time)
n/a
Published benchmark results for π0.5
BenchmarkResultSource
LIBEROAverage success rate across LIBERO-Spatial/Object/Goal/10 (%)96.85%github.comopenpi LIBERO README, π0.5 @ 30k fine-tuned checkpoint: Spatial 98.8, Object 98.2, Goal 98.0, 10 92.4; average 96.85. As of 17 Oct 2026
RoboArenaRoboArena leaderboard score (pairwise-comparison rating, DROID setup)1608robo-arena.github.ioOfficial RoboArena leaderboard, last updated 2026-10-17; policy entry 'pi05_droid', 745 evals, std 30.7. As of 17 Oct 2026
Physical Intelligence logo

π0-FAST

Open weightsVLA

Autoregressive variant of π0 that uses the FAST frequency-space action tokenizer; base and DROID checkpoints released in openpi.

Released
16 Jan 2025
Parameters
n/a
License
Apache-2.0
Embodiments
single-arm, bimanual, mobile manipulator
GitHub stars
14.3k as of 19 Oct 2026
HF downloads (all time)
n/a
Published benchmark results for π0-FAST
BenchmarkResultSource
RoboArenaRoboArena leaderboard score (pairwise-comparison rating, DROID setup)1582robo-arena.github.ioOfficial RoboArena leaderboard, last updated 2026-10-17; policy entry 'pi0_fast_droid', 941 evals, std 29.6. As of 17 Oct 2026
Physical Intelligence logo

π0

Open weightsVLA

Flow-matching VLA built on the PaliGemma 3B VLM plus a 300M action expert, trained on data from multiple robot platforms; weights released in openpi in February 2025.

Released
31 Oct 2024
Parameters
3.3B
License
Apache-2.0
Embodiments
single-arm, bimanual, mobile manipulator
GitHub stars
14.3k as of 19 Oct 2026
HF downloads (all time)
n/a
Published benchmark results for π0
BenchmarkResultSource
RoboArenaRoboArena leaderboard score (pairwise-comparison rating, DROID setup)1461robo-arena.github.ioOfficial RoboArena leaderboard, last updated 2026-10-17; policy entry 'pi0_droid', 1120 evals, std 29.8. As of 17 Oct 2026

Closed or unreleased-weights models (2)

Physical Intelligence logo

π0.7

Closed weightsVLA

Generalist VLA steerable through language, metadata, control-mode and visual-subgoal prompts; the company reports early compositional generalization and cross-embodiment transfer (e.g. laundry folding on a UR5e pair without UR5e training data).

Released
16 Apr 2026
Parameters
n/a
License
Proprietary
Embodiments
mobile manipulator, bimanual, single-arm
GitHub stars
n/a
HF downloads (all time)
n/a

No benchmark results on a shared public benchmark have been published for this model.

Physical Intelligence logo

π*0.6

Closed weightsVLA

π0.6-based VLA further trained with Recap (advantage-conditioned offline/online RL from demonstrations, corrections and autonomous rollouts) and evaluated on laundry folding, espresso making and box assembly.

Released
17 Nov 2025
License
Proprietary
Embodiments
bimanual, mobile manipulator
GitHub stars
n/a
HF downloads (all time)
n/a

No benchmark results on a shared public benchmark have been published for this model.