How close are we to general-purpose robotics?When will robots handle everyday work?
or, simply: When will robots handle everyday work?or, precisely: 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.
Figure 03 deploys for logistics at BMW Spartanburg - After the Figure 02 pilot, BMW put Figure 03 to work on complex logistics sequencing at Spartanburg, an expanded but still narrow, supervised deployment. Next up - First Neo units ship to homes (expected 2026).
State of playWhere general robotics stands right now
The current stage, the honest metric, and the single threshold that gates the next stage. Each threshold is a falsifiable claim with a named next test.How far up the ladder we've climbed, the honest verdict, and the one thing blocking the next step.
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.
No single honest number for this one - read where it stands by the milestones and the next test.No single honest scalar - progress is read by milestones and the named next test, not a headline figure.
Handle unfamiliar objects and toolsDexterous manipulation Next test: Independent long-horizon manipulation trials on tools and deformable objects, e.g. pi-0.6 field runs
The thresholds that gate the next stageWhat has to happen next
Each threshold is a falsifiable claim with a named next test; the gap chart shows how far today's metric sits from the goal.Each row is one thing that has to be proven — and how far today's number is from the target.
The record behind the verdict
Major events set large; context events set small but never hidden. Everything below the TODAY rule is a schedule, not a result.
Fixed industrial arms
Fixed industrial arms begins with unimate, the first industrial robot, joins a gm line. The result established the next question for the field.
Dynamic legged locomotion
Dynamic legged locomotion moved the field from honda unveils asimo to darpa robotics challenge finals (and the falls). The results narrowed the next question without closing it.
The vision-language-action turn
The vision-language-action turn moved the field from boston dynamics atlas runs parkour to open x-embodiment pools cross-robot data. The results narrowed the next question without closing it.
General-purpose humanoids
General-purpose humanoids moved the field from pi-0 code and weights released to first neo units ship to homes. The results narrowed the next question without closing it.
Events outside the declared eras
Events outside the declared eras moved the field from digit enters a multi-year warehouse deployment to tesla's stated goal: mass humanoid manufacturing. The results narrowed the next question without closing it.
Why the meters read the way they do
The learning curves and comparisons that justify each threshold's percentage. Every series is measured, with the source event linked in the timeline above.
BANKED · 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
OBSERVED EVIDENCERL whole-body control; Atlas, Unitree and others walk, run and recover from shoves
CURRENT FRONTIER
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
DECISIVE NEXT TESTIndependent long-horizon manipulation trials on tools and deformable objects, e.g. pi-0.6 field runs
DOWNSTREAM GATE
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
DECISIVE NEXT TESTPublished intervention-rate and uptime data from paid deployments
DOWNSTREAM GATE
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
DECISIVE NEXT TESTMulti-site customers renew deployments without vendor operators on site
Read the evidence more closely
Definitions, system boundaries and experimental caveats behind the headline record.
01Cross-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.
02Open 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.
03Helix 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.
04Gemini'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.
05On-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.
06Synthetic-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.
07Digit'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.
08Autonomy 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.
09Fifteen-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.
10Instrument 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.
11π₀.7 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.
If the remaining tests pass
Downstream capabilities, drawn dashed because they depend on results not yet in.
Who is building it-and what the money saysCapital, institutions and the global race
The teams doing the work, where they are based, and whether the money points to real delivery or only a plan.Company finance, public programmes, institutional leadership and market evidence-kept separate from valuations, forecasts and announced capacity.
General-purpose robotics is drawing large rounds, but commercial evidence is still dominated by narrow material-handling and manipulation workflows. US labs lead frontier robot policies, China leads manufacturing scale, and incumbent industrial automation remains far larger than the humanoid sector.
Who is building itCompanies, laboratories and programmes
Figure AI
USAHumanoid hardware, Helix vision-language-action models and factory/logistics deployments.
Apptronik
USAApollo humanoid for manufacturing and logistics, with Google DeepMind model collaboration.
Agility Robotics
USADigit humanoid and Arc fleet platform for repetitive warehouse movement.
| Player | Country | What they are doing | Funding | Named investors | Source |
|---|---|---|---|---|---|
| Figure AIcompany | USA | Humanoid hardware, Helix vision-language-action models and factory/logistics deployments. The valuation is not funding, and announced factory capacity is not achieved robot output. | Series C · >$1B · Sep 2025 · $39B post-money valuation | Parkway Venture Capital · Brookfield · NVIDIA · Macquarie Capital · Intel Capital | Source · figure.ai |
| Apptronikcompany | USA | Apollo humanoid for manufacturing and logistics, with Google DeepMind model collaboration. | Series A · $403M · Mar 2025 | B Capital · Capital Factory · Google · Mercedes-Benz · Japan Post Capital | Source · apptronik.com |
| Agility Roboticscompany | USA | Digit humanoid and Arc fleet platform for repetitive warehouse movement. A proposed SPAC valuation is not treated as a completed funding round. | Series B · $150M · Apr 2022 | DCVC · Playground Global · Amazon Industrial Innovation Fund · Sony Innovation Fund | Source · agilityrobotics.com |
| Physical Intelligencecompany | USA | Cross-embodiment pi-series foundation policies and deployment-specific adaptation. Funding is omitted because no primary company announcement verifying reported round totals was found. | Not disclosed | Not disclosed | Source · pi.website |
| Google DeepMind Roboticslab | UK / USA | Gemini Robotics, embodied reasoning and Open X-Embodiment research. Funded inside Alphabet; no standalone lab financing round. | Not disclosed | Not disclosed | Source · deepmind.google |
| NVIDIA Roboticslab | USA | GR00T open models, simulation, synthetic data and robot-compute platforms. NVIDIA corporate financing is not represented as robotics-lab funding. | Not disclosed | Not disclosed | Source · research.nvidia.com |
Where every number comes from
4 sources — every figure on this page traces to one.