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GP ROBOTICS

The problemsIntelligence & MachinesGeneral Robotics

Updated · July 2026

How close are we to general-purpose robotics?When will robots handle everyday work?

When will robots handle everyday work?How close are we to general-purpose robotics?

Legged locomotion is essentially solved, but dexterous manipulation and open-world autonomy are not, so today's factory and home robots run narrow, supervised, and heavily teleoperated.Robots can walk, run and keep their balance beautifully now. The hard part left is hands and judgment: doing varied tasks all day, safely, without a human quietly steering.

Current stageLab demoDemonstrated in the laboratoryShown to work in a lab
Read the evidence Where we stand
The path so far

Recorded milestones

  1. Atlas parkour

  2. Figure 03 logistics deployment at BMW

Open tests · conceptual branches

TODAY
  1. OPEN TEST02 ↗
    Handle unfamiliar objects and toolsDexterous manipulationRobots grab an apple almost every time, but a spoon, scissors or a fitted part still trips them up.Transfer fine-motor skills across unfamiliar objects, tools and cluttered scenes
  2. OPEN TEST03 ↗
    Work for hours without a human steeringOpen-world autonomyHome and factory robots still lean on remote operators and reset every 30-90 minutes. Boring, reliable shifts are the goal.Complete multi-hour tasks safely with rare human intervention or teleoperation
  3. OPEN TEST04 ↗
    Cost less than the work is worthLabor economicsA $20k price tag ignores the teleoperators, supervision and repairs that make a robot-hour actually work.Fully-loaded cost per useful hour competitive with human labor
TODAY
OPEN TEST02 ↗
Handle unfamiliar objects and toolsDexterous manipulationRobots grab an apple almost every time, but a spoon, scissors or a fitted part still trips them up.Transfer fine-motor skills across unfamiliar objects, tools and cluttered scenes
OPEN TEST03 ↗
Work for hours without a human steeringOpen-world autonomyHome and factory robots still lean on remote operators and reset every 30-90 minutes. Boring, reliable shifts are the goal.Complete multi-hour tasks safely with rare human intervention or teleoperation
OPEN TEST04 ↗
Cost less than the work is worthLabor economicsA $20k price tag ignores the teleoperators, supervision and repairs that make a robot-hour actually work.Fully-loaded cost per useful hour competitive with human labor
Milestones are in recorded order. Open tests are separate questions, not a forecast.Milestones are in date order. Open tests are separate tests, not a promise.
General RoboticsWhere we stand

The path shows Figure 03 logistics deployment at BMW. Open thresholds are separate tests in a conceptual map, not a forecast. No single percentage represents progress.

01 · Where we standWhere we stand

Where we standWhere we stand

The current stage, the evidence recorded, and the single threshold that gates the next stage.The stage reached, the evidence recorded, and the one test still blocking the next step.

What the evidence has shownWhat the evidence has shown

Robots can walk, run and keep their balance beautifully now. The hard part left is hands and judgment: doing varied tasks all day, safely, without a human quietly steering.Legged locomotion is essentially solved, but dexterous manipulation and open-world autonomy are not, so today's factory and home robots run narrow, supervised, and heavily teleoperated.

1 of 4 tests recorded1 of 4 thresholds achieved open
✓Walk, run and keep balance like an animalDynamic locomotion & balanceAchieved
Handle unfamiliar objects and toolsDexterous manipulationOpen test
Work for hours without a human steeringOpen-world autonomyOpen test
Cost less than the work is worthLabor economicsOpen test

The next testBlocking threshold

Handle unfamiliar objects and toolsDexterous manipulation

Independent long-horizon manipulation trials on tools and deformable objects, e.g. pi-0.6 field runsIndependent long-horizon manipulation trials on tools and deformable objects, e.g. pi-0.6 field runs

02 · Thresholds & evidenceThresholds & evidence

What would count as proof?What would count as proof?

A clear test, the evidence behind it, and the limits of the claim.A clear test, the evidence behind it, and what it still leaves open.

✓Achieved · 2024

Walk, run and keep balance like an animalDynamic locomotion & balance

Getting around on two legs used to be the famous hard problem. Reinforcement learning has mostly cracked it.Robust bipedal walking, running and whole-body recovery over uneven terrain

Recorded result RL whole-body control; Atlas, Unitree and others walk, run and recover from shoves

SOURCE RECORD

Boston Dynamics x RAI Institute: RL for Atlas (2026) ↗Timeline · Boston Dynamics Atlas runs parkour ↗Timeline · Unitree G1 drops to $16,000 ↗

Still to demonstrateWhat this does not establishA separate system can still have different conditions and results.The named measurement has no wider implication until the supporting system test is also shown.

View every evidence record

Achieved

Walk, run and keep balance like an animalDynamic locomotion & balance

Getting around on two legs used to be the famous hard problem. Reinforcement learning has mostly cracked it.Robust bipedal walking, running and whole-body recovery over uneven terrain

Recorded result: 2024 · RL whole-body control; Atlas, Unitree and others walk, run and recover from shoves

Boston Dynamics x RAI Institute: RL for Atlas (2026) ↗Timeline · Boston Dynamics Atlas runs parkour ↗Timeline · Unitree G1 drops to $16,000 ↗

Open test

Handle unfamiliar objects and toolsDexterous manipulation

Robots grab an apple almost every time, but a spoon, scissors or a fitted part still trips them up.Transfer fine-motor skills across unfamiliar objects, tools and cluttered scenes

Next test: Independent long-horizon manipulation trials on tools and deformable objects, e.g. pi-0.6 field runs

Open test

Work for hours without a human steeringOpen-world autonomy

Home and factory robots still lean on remote operators and reset every 30-90 minutes. Boring, reliable shifts are the goal.Complete multi-hour tasks safely with rare human intervention or teleoperation

Next test: Published intervention-rate and uptime data from paid deployments

Open test

Cost less than the work is worthLabor economics

A $20k price tag ignores the teleoperators, supervision and repairs that make a robot-hour actually work.Fully-loaded cost per useful hour competitive with human labor

Next test: Multi-site customers renew deployments without vendor operators on site

03 · The recordThe record

The record behind the verdictThe record behind the verdict

Major milestones and smaller context events share one source-linked record. Scheduled targets are marked separately from recorded events.Milestones, smaller events and setbacks share one sourced record. Scheduled targets are marked separately from recorded events.

Event key
TheoryExperimentFundingSetbackPolicyDeployment Future / scheduled
1961-19991 event1 shown

Fixed industrial arms

Recorded event: “Unimate, the first industrial robot, joins a GM line”.

1961
Unimate, the first industrial robot, joins a GM lineDeployment
Unimation's hydraulic Unimate arm began unloading a die-casting machine at a General Motors plant, launching factory robotics.
Unimation / General Motors
2000-20202 events1 shown

Dynamic legged locomotion

This record spans “Honda unveils ASIMO” to “DARPA Robotics Challenge Finals (and the falls)”.

2000
Honda unveils ASIMOExperiment
Honda revealed ASIMO, a 120cm bipedal humanoid that could walk, climb stairs and recognize faces, becoming the era's icon of legged robotics.
Honda
2015
DARPA Robotics Challenge Finals (and the falls)
At the Pomona finals most humanoids struggled with doors, valves and rubble, and a widely shared reel of robots toppling over exposed how brittle whole-body autonomy still was.
DARPA
2021-20244 events2 shown

The vision-language-action turn

This record spans “Boston Dynamics Atlas runs parkour” to “Open X-Embodiment pools cross-robot data”.

2021
Boston Dynamics Atlas runs parkourExperiment
Two Atlas robots vaulted, leapt gaps and did backflips through a parkour course, showcasing dynamic whole-body control (though heavily choreographed and hydraulic).
Boston Dynamics
2022
Tesla shows first Optimus prototype
At AI Day a rough Optimus prototype walked onstage and waved; Tesla framed it as an early development robot, kicking off the commercial humanoid race.
Tesla
2023
Google DeepMind's RT-2 vision-language-action modelTheory
RT-2 fused web-scale vision-language pretraining with robot data so a single model could turn natural-language commands into actions and generalize to unseen objects, opening the VLA era.
Google DeepMind
2023
Open X-Embodiment pools cross-robot data
A shared corpus combined more than one million real-robot trajectories from 60 datasets across 22 embodiments, enabling the first major cross-robot training effort.
Google DeepMind and 34 research labs
2025-202714 events3 shown

General-purpose humanoids

This record spans “pi-0 code and weights released” to “First Neo units ship to homes” and includes scheduled targets.

2025
pi-0 code and weights released
Physical Intelligence published pi-0's code, weights and fine-tuned checkpoints, making its cross-embodiment VLA reproducible outside the originating lab, with explicit transfer limitations.
Physical Intelligence
2025
Helix controls a humanoid upper body
One VLA controlled a humanoid's torso, wrists, head and fingers and coordinated two robots without task-specific fine-tuning; this was a vendor lab demonstration.
Figure AI
2025
Gemini Robotics adds direct actions
Google DeepMind added robot actions to Gemini 2.0 and demonstrated language-steerable manipulation across ALOHA, Franka and an adapted Apollo humanoid in lab evaluations.
Google DeepMind and Apptronik
2025
GR00T N1 released as an open model
NVIDIA released an open-weight humanoid foundation model trained on human videos plus real, simulated and synthetic robot trajectories; results remained developer-evaluated.
NVIDIA
2025
Physical Intelligence pi-0.5 generalizes to new homesTheory
pi-0.5 extended the VLA approach with open-world generalization, letting a robot clean kitchens and bedrooms in homes it had never seen during training.
Physical Intelligence
2025
Gemini Robotics runs on-device
An on-device VLA ran locally and opened to trusted testers, with adaptation demonstrated from 50 to 100 examples; this was not a paid-deployment reliability result.
Google DeepMind
2025
1X opens orders for the Neo home robot (~$20k)Deployment
1X began selling the Neo humanoid for $20,000; crucially, tasks Neo can't do autonomously are performed by a remote human teleoperator who can see into the home.
1X Technologies
2025
Digit passes 100,000 totes moved
Agility reported more than 100,000 totes moved at GXO's Flowery Branch facility, cumulative throughput for a constrained workflow without published intervention rate or robot-hours.
Agility Robotics and GXO
2026
pi-0.6 reaches customer-site tasks
A packaging robot completed a partner-reported shift at 96.4% autonomy while laundry-folding systems worked in commercial laundromats; both remained narrow, human-supported workflows.
Physical Intelligence, Ultra and Weave Robotics
2026
VLA memory reaches 15-minute tasks
Multi-scale Embodied Memory demonstrated kitchen and meal-preparation tasks requiring up to 15 minutes of context, still far short of unsupervised work-shift autonomy.
Physical Intelligence
2026
Gemini Robotics-ER 1.6 opens by API
The embodied-reasoning component became API-accessible for spatial reasoning, task planning, success detection and instrument reading; it was not an end-to-end robot deployment.
Google DeepMind
2026
pi-0.7 recombines manipulation skills
One generalist policy recombined learned skills across robots, scenes and tasks without specialist fine-tuning; the evidence was an internal evaluation, not independent open-world validation.
Physical Intelligence
2026
Figure 03 deploys for logistics at BMW SpartanburgDeploymentWe are here
After the Figure 02 pilot, BMW put Figure 03 to work on complex logistics sequencing at Spartanburg, an expanded but still narrow, supervised deployment.
Figure AI / BMW
2026
First Neo units ship to homesDeploymentTarget
1X plans to begin delivering Neo to early-access buyers during 2026, one of the first humanoids in ordinary households, initially leaning heavily on teleoperation.
1X Technologies
2024-20276 events1 shown

Additional records

This record spans “Digit enters a multi-year warehouse deployment” to “Tesla's stated goal: mass humanoid manufacturing” and includes scheduled targets.

2024
Digit enters a multi-year warehouse deployment
GXO signed a Robots-as-a-Service agreement for Digit robots moving totes at a live SPANX facility; the achieved deployment covered one constrained workflow.
GXO and Agility Robotics
2024
Unitree G1 drops to $16,000
Unitree launched the G1 humanoid at a base price of $16,000, an order-of-magnitude cut that put walking humanoids within reach of labs and small firms.
Unitree Robotics
2024
Figure 02 pilots at BMW SpartanburgDeployment
Figure 02 ran a real production pilot at BMW's South Carolina plant, inserting sheet-metal parts into fixtures; it was a limited, closely supervised trial rather than full autonomy.
Figure AI / BMW
2024
Physical Intelligence releases pi-0
Physical Intelligence's pi-0 generalist policy ran one model across multiple robots to fold laundry, bus tables and bag groceries, demonstrating cross-embodiment dexterous skills.
Physical Intelligence
2024
4 million industrial robots now in factories
The IFR logged a record 4.28 million operational industrial robots worldwide, the fixed-arm base on which the humanoid wave is building.
International Federation of Robotics
2027
Tesla's stated goal: mass humanoid manufacturingFundingTarget
Tesla says a next-generation line is designed for up to 10 million Optimus units per year at a ~$20k target cost; this is an aspirational goal, not a demonstrated capability (Tesla has missed every prior Optimus timeline).
Tesla
— end of record · 27 events shown —
04 · The dataThe data

The data behind the verdictThe data behind the verdict

Read each measure with its units, conditions, uncertainty and source; targets remain distinct from observations.Read each chart with its conditions, limits and source. Future targets remain distinct from recorded results.

Methods & conditions

Under the measurement.

The boundary of a measurement changes what it means.

Technical note 1 of 11

Cross-robot gains are benchmark-relative

Method and conditions

Open X-Embodiment combined 60 datasets from 34 labs and 22 embodiments. RT-1-X improved performance by 50% in small-data evaluations, while RT-2-X scored 3× RT-2 on the project's emergent-skill evaluation. These are relative benchmark results, not absolute reliability rates.

Source notes
Open X-Embodiment

Technical notes and sources

Each note keeps its authored method, conditions and citations available for review.

Cross-robot gains are benchmark-relative

Open X-Embodiment combined 60 datasets from 34 labs and 22 embodiments. RT-1-X improved performance by 50% in small-data evaluations, while RT-2-X scored 3× RT-2 on the project's emergent-skill evaluation. These are relative benchmark results, not absolute reliability rates.

Open X-Embodiment
Open π₀ still needs local robot data

Open π₀'s authors report that adapting it to individual tasks generally required 1-20 hours of robot data, but explicitly warn that adaptation to outside platforms may fail.

Physical Intelligence
Helix splits reasoning from control

Helix separates slower semantic reasoning from fast motor control: its onboard VLM runs at 7-9 Hz, while the action system handles high-rate continuous upper-body control. “Pick up virtually anything” remains Figure's wording, not an independently bounded success rate.

Figure AI
Gemini's gains lack deployment measures

Gemini Robotics reportedly more than doubled average performance over previous VLAs on DeepMind's generalization benchmark; Gemini Robotics-ER achieved 2-3× Gemini 2.0's success rate in an end-to-end internal setup. Neither result supplies paid-deployment uptime or intervention data.

Google DeepMind
On-device transfer followed adaptation

Gemini Robotics On-Device's nine-task internal evaluation reported roughly 0.52-0.74 success rates, versus approximately 0.11-0.36 for the previous on-device baseline. It was trained principally for ALOHA and then adapted to Franka and Apollo, so this is transfer after adaptation-not zero-shot embodiment interchangeability.

Google DeepMind
Synthetic-data gains are model-level

NVIDIA generated 780,000 synthetic trajectories, described as equivalent to 6,500 demonstration-hours, in 11 hours; mixing them with real data improved GR00T N1 by 40% against NVIDIA's real-data-only baseline. This is an internal model result, not a 40% improvement in deployed labor productivity.

NVIDIA
Digit's deployment metrics remain sparse

Digit's 100,000-tote result demonstrates repeated production cycles, but the published workflow remains tote pickup, carrying and placement integrated with AMRs and conveyors. Agility does not publish intervention rate, robot-hours, fleet size, throughput per robot or cost per tote.

Agility Robotics
Autonomy still includes human recovery

In the π₀.6 partner results, incorporating deployment-specific laundry data reduced missed-grasp sequences by 42% and interventions by 50%. Human-in-the-loop operators still handle failures, making intervention frequency-not binary “autonomous” labeling-the important variable.

Physical Intelligence
Fifteen-minute memory is not a work shift

Multi-scale Embodied Memory retains recent observations through a video encoder but compresses long-term state into natural-language memories selected by the model. Its demonstrated horizon is up to 15 minutes, still well short of an unsupervised work shift.

Physical Intelligence
Instrument reading is a component test

Gemini Robotics-ER 1.6's internal instrument-reading score rose from 23% for ER 1.5 to 86%, or 93% with agentic vision. This evaluates perception and reasoning over instrument images; it does not demonstrate autonomous physical inspection end to end.

Google DeepMind
π₀.7 separates heterogeneous behaviours

π₀.7's key mechanism is multimodal conditioning-language, strategy metadata, desired episode duration and visual subgoals-which lets heterogeneous human, robot and autonomous-experience data coexist without conflating different behaviours. Its claimed compositional generalization remains developer-evaluated.

Physical Intelligence
05 · What it unlocksWhat it unlocks

If the remaining tests pass.If the remaining tests pass.

Downstream capabilities remain conditional on results that are not yet in.These are possible next capabilities, conditional on results that are not yet in.

TODAY
  1. PASSED · 202401 ↗
    Walk, run and keep balance like an animalDynamic locomotion & balanceGetting around on two legs used to be the famous hard problem. Reinforcement learning has mostly cracked it.Robust bipedal walking, running and whole-body recovery over uneven terrain
  2. OPEN TEST02 ↗
    Handle unfamiliar objects and toolsDexterous manipulationRobots grab an apple almost every time, but a spoon, scissors or a fitted part still trips them up.Transfer fine-motor skills across unfamiliar objects, tools and cluttered scenes
  3. OPEN TEST03 ↗
    Work for hours without a human steeringOpen-world autonomyHome and factory robots still lean on remote operators and reset every 30-90 minutes. Boring, reliable shifts are the goal.Complete multi-hour tasks safely with rare human intervention or teleoperation
  4. OPEN TEST04 ↗
    Cost less than the work is worthLabor economicsA $20k price tag ignores the teleoperators, supervision and repairs that make a robot-hour actually work.Fully-loaded cost per useful hour competitive with human labor
PASSED · 202401 ↗
Walk, run and keep balance like an animalDynamic locomotion & balanceGetting around on two legs used to be the famous hard problem. Reinforcement learning has mostly cracked it.Robust bipedal walking, running and whole-body recovery over uneven terrain
TODAY
OPEN TEST02 ↗
Handle unfamiliar objects and toolsDexterous manipulationRobots grab an apple almost every time, but a spoon, scissors or a fitted part still trips them up.Transfer fine-motor skills across unfamiliar objects, tools and cluttered scenes
OPEN TEST03 ↗
Work for hours without a human steeringOpen-world autonomyHome and factory robots still lean on remote operators and reset every 30-90 minutes. Boring, reliable shifts are the goal.Complete multi-hour tasks safely with rare human intervention or teleoperation
OPEN TEST04 ↗
Cost less than the work is worthLabor economicsA $20k price tag ignores the teleoperators, supervision and repairs that make a robot-hour actually work.Fully-loaded cost per useful hour competitive with human labor

Possible, if the open tests pass.

  1. Safer dangerous work

    This is a possible benefit only if safer dangerous work works safely, reliably and at useful scale.Potential benefit only if the stated use (Safer dangerous work) is demonstrated with evidence for safety, reliability, affordability and scale.

  2. Abundant physical labor

    This is a possible benefit only if abundant physical labor works safely, reliably and at useful scale.Potential benefit only if the stated use (Abundant physical labor) is demonstrated with evidence for safety, reliability, affordability and scale.

  3. Independent living support

    This is a possible benefit only if independent living support works safely, reliably and at useful scale.Potential benefit only if the stated use (Independent living support) is demonstrated with evidence for safety, reliability, affordability and scale.

These are possible outcomes, if the open tests pass. They are conditional, not promises.
06 · The players & the moneyThe players & the money

Meet the builders.Meet the builders.

The organisations, their approaches, and the evidence behind them.The teams doing the work, the money around them, and what the record can support.

USA · company

Figure AI

Humanoid hardware, Helix vision-language-action models and factory/logistics deployments.

Funding contextSeries C · >$1B · Sep 2025 · $39B post-money valuationThe valuation is not funding, and announced factory capacity is not achieved robot output.
Named investorsParkway Venture Capital · Brookfield · NVIDIA · Macquarie Capital · Intel Capital

Capital is context, not a demonstration.

Company source ↗
View every builder record

Figure AI

USA · company · Humanoid hardware, Helix vision-language-action models and factory/logistics deployments.

Funding: Series C · >$1B · Sep 2025 · $39B post-money valuation

Named investors: Parkway Venture Capital · Brookfield · NVIDIA · Macquarie Capital · Intel Capital

The valuation is not funding, and announced factory capacity is not achieved robot output.

Company source ↗

Apptronik

USA · company · Apollo humanoid for manufacturing and logistics, with Google DeepMind model collaboration.

Funding: Series A · $403M · Mar 2025

Named investors: B Capital · Capital Factory · Google · Mercedes-Benz · Japan Post Capital

Company source ↗

Agility Robotics

USA · company · Digit humanoid and Arc fleet platform for repetitive warehouse movement.

Funding: Series B · $150M · Apr 2022

Named investors: DCVC · Playground Global · Amazon Industrial Innovation Fund · Sony Innovation Fund

A proposed SPAC valuation is not treated as a completed funding round.

Company source ↗

Physical Intelligence

USA · company · Cross-embodiment pi-series foundation policies and deployment-specific adaptation.

Funding: Funding not recorded in this record.

Funding is omitted because no primary company announcement verifying reported round totals was found.

Company source ↗

Google DeepMind Robotics

UK / USA · lab · Gemini Robotics, embodied reasoning and Open X-Embodiment research.

Funding: Funding not recorded in this record.

Funded inside Alphabet; no standalone lab financing round.

Company source ↗

NVIDIA Robotics

USA · lab · GR00T open models, simulation, synthetic data and robot-compute platforms.

Funding: Funding not recorded in this record.

NVIDIA corporate financing is not represented as robotics-lab funding.

Company source ↗
07 · SourcesSources

Follow the evidence.Follow the evidence.

48 sources — every figure on this page traces to one.48 sources — every figure on this page traces to one.

Source · what this page citesScope and limits
  1. 01

    BMW Group: Figure 03 physical-AI project at Spartanburg (2026)

    Read source ↗press.bmwgroup.com
    Used on this page
    All 3 uses across 2 destinations

    Scope: Not recorded. Limit: Not recorded.

  2. 02

    Physical Intelligence, pi-0.5: a VLA with open-world generalization (arXiv 2504.16054)

    Read source ↗arxiv.org
    Used on this page
    All 2 uses across 2 destinations

    Scope: Not recorded. Limit: Not recorded.

  3. 03

    Boston Dynamics x RAI Institute: RL for Atlas (2026)

    Read source ↗rai-inst.com
    Used on this page
    All 1 use across 1 destination

    Scope: Not recorded. Limit: Not recorded.

  4. 04

    Scope: Not recorded. Limit: Not recorded.

  5. 05

    Timeline · Unimate, the first industrial robot, joins a GM line

    Read source ↗spectrum.ieee.org
    Used on this page
    All 2 uses across 1 destination

    Scope: Not recorded. Limit: Not recorded.

  6. 06

    Timeline · Honda unveils ASIMO

    Read source ↗global.honda
    Used on this page
    All 2 uses across 2 destinations

    Scope: Not recorded. Limit: Not recorded.

  7. 07

    Timeline · DARPA Robotics Challenge Finals (and the falls)

    Read source ↗spectrum.ieee.org
    Used on this page
    All 2 uses across 2 destinations

    Scope: Not recorded. Limit: Not recorded.

  8. 08

    Scope: Not recorded. Limit: Not recorded.

  9. 09

    Timeline · Tesla shows first Optimus prototype

    Read source ↗cnbc.com
    Used on this page
    All 2 uses across 2 destinations

    Scope: Not recorded. Limit: Not recorded.

  10. 10

    Timeline · Google DeepMind's RT-2 vision-language-action model

    Read source ↗deepmind.google
    Used on this page
    All 2 uses across 2 destinations

    Scope: Not recorded. Limit: Not recorded.

  11. 11

    Timeline · Open X-Embodiment pools cross-robot data

    Read source ↗robotics-transformer-x.github.io
    Used on this page
    All 2 uses across 2 destinations

    Scope: Not recorded. Limit: Not recorded.

  12. 12

    Timeline · Digit enters a multi-year warehouse deployment

    Read source ↗investors.gxo.com
    Used on this page
    All 2 uses across 2 destinations

    Scope: Not recorded. Limit: Not recorded.

  13. 13

    Scope: Not recorded. Limit: Not recorded.

  14. 14

    Scope: Not recorded. Limit: Not recorded.

  15. 15

    Timeline · Physical Intelligence releases pi-0

    Read source ↗pi.website
    Used on this page
    All 2 uses across 2 destinations

    Scope: Not recorded. Limit: Not recorded.

  16. 16

    Timeline · 4 million industrial robots now in factories

    Read source ↗ifr.org
    Used on this page
    All 2 uses across 2 destinations

    Scope: Not recorded. Limit: Not recorded.

  17. 17

    Timeline · pi-0 code and weights released

    Read source ↗pi.website
    Used on this page
    All 2 uses across 2 destinations

    Scope: Not recorded. Limit: Not recorded.

  18. 18

    Timeline · Helix controls a humanoid upper body

    Read source ↗figure.ai
    Used on this page
    All 2 uses across 2 destinations

    Scope: Not recorded. Limit: Not recorded.

  19. 19

    Timeline · Gemini Robotics adds direct actions

    Read source ↗deepmind.google
    Used on this page
    All 2 uses across 2 destinations

    Scope: Not recorded. Limit: Not recorded.

  20. 20

    Scope: Not recorded. Limit: Not recorded.

  21. 21

    Scope: Not recorded. Limit: Not recorded.

  22. 22

    Timeline · Gemini Robotics runs on-device

    Read source ↗deepmind.google
    Used on this page
    All 2 uses across 2 destinations

    Scope: Not recorded. Limit: Not recorded.

  23. 23

    Timeline · Digit passes 100,000 totes moved

    Read source ↗agilityrobotics.com
    Used on this page
    All 2 uses across 2 destinations

    Scope: Not recorded. Limit: Not recorded.

  24. 24

    Timeline · pi-0.6 reaches customer-site tasks

    Read source ↗pi.website
    Used on this page
    All 2 uses across 2 destinations

    Scope: Not recorded. Limit: Not recorded.

  25. 25

    Scope: Not recorded. Limit: Not recorded.

  26. 26

    Timeline · Gemini Robotics-ER 1.6 opens by API

    Read source ↗deepmind.google
    Used on this page
    All 2 uses across 2 destinations

    Scope: Not recorded. Limit: Not recorded.

  27. 27

    Timeline · pi-0.7 recombines manipulation skills

    Read source ↗pi.website
    Used on this page
    All 2 uses across 2 destinations

    Scope: Not recorded. Limit: Not recorded.

  28. 28

    Scope: Not recorded. Limit: Not recorded.

  29. 29

    Timeline · Tesla's stated goal: mass humanoid manufacturing

    Read source ↗electrek.co
    Used on this page
    All 2 uses across 2 destinations

    Scope: Not recorded. Limit: Not recorded.

  30. 30

    Engineer note · Synthetic-data gains are model-level

    Read source ↗nvidianews.nvidia.com

    Scope: Not recorded. Limit: Not recorded.

  31. 31

    Scope: Not recorded. Limit: Not recorded.

  32. 32

    companies · Apptronik

    Read source ↗apptronik.com
    Used on this page
    All 2 uses across 1 destination

    Scope: Not recorded. Limit: Not recorded.

  33. 33

    companies · Agility Robotics

    Read source ↗agilityrobotics.com
    Used on this page
    All 2 uses across 1 destination

    Scope: Not recorded. Limit: Not recorded.

  34. 34

    companies · Physical Intelligence

    Read source ↗pi.website
    Used on this page
    All 1 use across 1 destination

    Scope: Not recorded. Limit: Not recorded.

  35. 35

    companies · Google DeepMind Robotics

    Read source ↗deepmind.google
    Used on this page
    All 2 uses across 1 destination

    Scope: Not recorded. Limit: Not recorded.

  36. 36

    people · Jeff Cardenas

    Read source ↗apptronik.com
    Used on this page
    All 1 use across 1 destination

    Scope: Not recorded. Limit: Not recorded.

  37. 37

    people · Jonathan Hurst

    Read source ↗agilityrobotics.com
    Used on this page
    All 1 use across 1 destination

    Scope: Not recorded. Limit: Not recorded.

  38. 38

    market · 4.66M industrial robots operating worldwide

    Read source ↗ifr.org
    Used on this page
    All 2 uses across 2 destinations

    Scope: Not recorded. Limit: Not recorded.

  39. 39

    market · Professional service-robot sales grew 9%

    Read source ↗ifr.org
    Used on this page
    All 1 use across 1 destination

    Scope: Not recorded. Limit: Not recorded.

  40. 40

    market · China reports more than 300 humanoid products

    Read source ↗english.www.gov.cn
    Used on this page
    All 1 use across 1 destination

    Scope: Not recorded. Limit: Not recorded.

  41. 41

    market · $5T humanoid market scenario by 2050

    Read source ↗apnews.com
    Used on this page
    All 2 uses across 2 destinations

    Scope: Not recorded. Limit: Not recorded.

  42. 42

    geography · European Union

    Read source ↗eur-lex.europa.eu
    Used on this page
    All 1 use across 1 destination

    Scope: Not recorded. Limit: Not recorded.

  43. 43

    Figure AI official site

    Read source ↗figure.ai

    Scope: Not recorded. Limit: Not recorded.

  44. 44

    Apptronik official site

    Read source ↗apptronik.com

    Scope: Not recorded. Limit: Not recorded.

  45. 45

    Agility Robotics official site

    Read source ↗agilityrobotics.com

    Scope: Not recorded. Limit: Not recorded.

  46. 46

    Google DeepMind official site

    Read source ↗deepmind.google

    Scope: Not recorded. Limit: Not recorded.

  47. 47

    Google DeepMind identity

    Read source ↗deepmind.google

    Scope: Not recorded. Limit: Not recorded.

  48. 48

    NVIDIA Robotics official site

    Read source ↗research.nvidia.com

    Scope: Not recorded. Limit: Not recorded.

Sources describe evidence. Inclusion is not endorsement.