Gravis Robotics rejser $200 millioner i Serie A for at skalere autonomt tungt maskineri

Gravis Robotics har rejst $200 millioner i Serie A-finansiering fra SoftBank, hvilket giver det Zürich-baserede bygge-robotikfirma betydelig ny kapital til at udvide sin teknologi for autonome tunge maskiner på globale infrastrukturprojekter.

Virksomheden beskriver finansieringen som den største Serie A i bygge-robotik til dato. Runden kommer på et tidspunkt, hvor investeringer i fysisk AI i stigende grad udvider sig ud over humanoide robotter og lagerautomatisering til industrier, hvor maskiner skal interagere med komplekse, skiftende miljøer.

For Gravis er det miljøet byggepladsen. I stedet for at bygge nyt tungt udstyr fra bunden, udvikler virksomheden hardware og AI-software, der er designet til at eftermontere eksisterende gravemaskiner og andet jordarbejdsudstyr, og dermed effektivt tilføje varierende grader af autonom drift til de flåder, entreprenører allerede ejer.

The new funding will be used to accelerate Gravis’ international rollout, expand its engineering team, and put its autonomous systems into more construction and infrastructure projects.

Bringer fysisk AI til tungt byggeri

Founded in late 2022 as a spinout from ETH Zurich, Gravis emerged from research into robotics and autonomous control systems. The company is led by CEO and co-founder Ryan Luke Johns and CTO and co-founder Dominic Jud, with robotics researcher Marco Hutter serving as a co-founder and board member.

Byggeri udgør en anden udfordring end mange af de miljøer, hvor autonome maskiner har fået fodfæste.

Et selvkørende køretøj forsøger for eksempel typisk at forstå og sikkert navigere i et miljø uden at ændre det. En gravemaskine forventes at gøre det modsatte. Hver spandbevægelse ændrer terrænet, som maskinen skal forstå næste gang.

Maskinen kan også støde på jord, sten, skiftende skråninger, underjordisk modstand og andre forhold, som ikke kan forudsiges fuldstændigt på forhånd.

Gravis forsøger at tackle denne usikkerhed gennem læringsbaserede robotkontrolsystemer, der kombinerer data fra maskinens hydraulik med LiDAR, kameraer og GNSS-positionering. Softwaren fortolker løbende maskinens omgivelser og de fysiske kræfter, der påvirker den, i stedet for blot at udføre en forudbestemt sekvens af bevægelser.

The company says its models are also trained extensively in simulation, allowing the AI to encounter large numbers of virtual excavation scenarios before being deployed to physical machinery.

Ombygning af eksisterende gravemaskiner til autonome maskiner

A central part of Gravis’ strategy is avoiding dependence on a single equipment manufacturer.

Byggefloater består typisk af maskiner fra flere mærker, ofte samlet gennem årtiers køb og lejeaftaler. At bede entreprenører om at erstatte disse flåder med specialbygget autonomt udstyr kunne gøre adoption væsentligt sværere.

I stedet har Gravis udviklet Gravis RACK, et modulært autonomt kontrolsystem, der kan installeres på eksisterende gravemaskiner og hjullæssere. Virksomheden siger, at platformen allerede er tilpasset udstyr fra producenter som Caterpillar, John Deere, JCB, Hitachi, Volvo, Yanmar, Case, Develon og Sumitomo.

Det tagmonterede system kombinerer kameraer, 3D LiDAR, GNSS RTK-positionering og edge‑computing i bilkvalitet. Fordi behandlingen foregår på maskinen, siger Gravis, at autonome funktioner kan fortsætte med at fungere, selv når pålidelig forbindelse ikke er tilgængelig – en vigtig overvejelse på fjerntliggende eller ufærdige byggepladser.

Fuld LiDAR-dækning og sensorfusion bruges til løbende at scanne det omkringliggende terræn. Disse informationer kan understøtte autonom udgravning, samtidig med at de producerer 3D‑site‑data, cut‑and‑fill‑visualiseringer og registre over udført arbejde.

Gravis’ perceptionssystem kan også identificere dump‑trucks og koordinere, hvor udgravet materiale skal placeres, så autonome arbejdsgange kan udvide sig ud over gravning til aktiviteter som læsning og materialehåndtering.

Autonomi uden at fjerne operatøren

Gravis is not positioning autonomy as an all-or-nothing transition.

Its Slate tablet interface allows contractors to move between several operating modes depending on the task. Operators can remain inside the cab and use AI-assisted guidance, step away while the machine performs longer autonomous tasks, or supervise equipment remotely.

In its in-cab Copilot mode, operators can use tap-to-dig controls and augmented visual guidance. Jobs can be defined through Computer-Aided Design (CAD) or Building Information Modeling (BIM) geometry, physical reference points or coordinates. The interface can then visualize how much material needs to be removed or added while the machine works.

At the other end of the spectrum, remote orchestration allows an operator to supervise and remotely operate one or more machines using live video and site information.

That hybrid approach may prove important to adoption. Construction sites rarely consist of repetitive tasks performed under perfectly controlled conditions. Keeping humans within the operational loop gives contractors a way to introduce autonomy gradually rather than redesigning an entire jobsite around fully driverless equipment.

Et softwarelag på tværs af blandede bygge‑flåder

The broader ambition is effectively to create an intelligence layer that sits above the fragmented heavy-equipment market.

If the same autonomous software can operate machinery from numerous manufacturers and across multiple machine sizes, contractors would potentially be able to deploy automation without standardizing their entire fleet around one vendor.

Gravis says its learning-based control system adjusts to the physical characteristics of different machines rather than requiring each excavator to be programmed independently.

That distinction could become increasingly important as physical AI moves from demonstrations into commercial deployment. A robotic system that performs well on one carefully configured machine is considerably less useful to large contractors than software capable of adapting to the heterogeneous fleets already operating around the world.

Gravis says its terrain-aware excavation technology can increase throughput by as much as 30%, although actual gains will inevitably depend on the machinery, job and site conditions.

Fra forskningsprojekter til aktive byggepladser

Gravis had already begun expanding commercially before the SoftBank investment.

In november 2025, the company announced $23 million in fresh funding alongside partnerships and deployments involving companies including Holcim, Taylor Woodrow, HD Hyundai and Flannery Plant Hire. At the time, Gravis said its systems were live in seven countries spanning the UK, European Union, United States, Latin America and Asia.

One notable deployment involved autonomous excavation on an active Taylor Woodrow infrastructure project at Manchester Airport. The company has also worked on autonomous quarry-material handling and established a partnership with Flannery designed to make excavators equipped with Gravis technology available through the equipment rental market.

That commercialization history helps distinguish the latest financing from physical-AI investments centered primarily on future prototypes. Gravis is raising capital while its systems are already being tested and used alongside conventional construction crews.

UK‑projekt vil sætte autonome gravemaskiner i en større prøve

The expansion is also receiving government support.

Gravis and Flannery Plant Hire were recently selected for a project under the UK’s CAM Pathfinder connected and automated mobility program. The initiative will trial a fully autonomous earthmoving system across six excavators, with applications including trenching, bulk excavation and truck loading.

The project is particularly relevant because equipment rental could become an important distribution channel for construction autonomy.

Rather than requiring contractors to purchase autonomous machines or permanently retrofit their own fleets, rental companies could provide autonomy-equipped machinery for individual projects. That could lower the financial and operational barriers to experimenting with the technology.

It would also expose autonomous systems to a much wider variety of jobsites, machinery configurations and ground conditions, potentially generating valuable operational data for improving the underlying AI models.

Hvorfor byggeri bliver et stort marked for fysisk AI

Much of the attention surrounding physical AI has focused on humanoid robots. Heavy machinery represents another potentially significant opportunity, and one where the economic use case may be easier to define.

Excavators, loaders and other machines already perform enormous amounts of physical work. The challenge is not inventing a new mechanical form factor but adding intelligence to equipment that is already deployed at scale.

That creates a different route to commercialization. Instead of asking customers to determine what a new type of robot should do, companies such as Gravis are targeting tasks that contractors already pay skilled workers and expensive machinery to perform every day.

The remaining challenge is reliability. Construction environments are messy, dynamic and safety-critical, which means autonomous equipment needs to operate consistently across conditions that are far less predictable than factory floors or warehouses.

Gravis’ focus on simulation, machine telemetry, sensor fusion and adaptable control models reflects just how difficult that problem is.

SoftBank‑finansiering bringer Gravis ind i en ny ekspansionsfase

The $200 million Series A gives Gravis considerably more resources to tackle that challenge.

Rather than simply funding further research, much of the opportunity now lies in deployment: installing systems across more equipment brands, accumulating experience from active construction projects and proving that autonomous machinery can deliver consistent economic benefits outside controlled demonstrations.

The retrofit strategy could be especially important. Construction companies have enormous amounts of capital tied up in existing machinery, and a platform capable of adding intelligence across those assets could scale differently from competitors requiring customers to purchase entirely new robotic fleets.

If that model proves reliable, construction may become one of physical AI’s more consequential markets. The machines are already there, the work is already defined, and demand for infrastructure continues to grow.

Gravis Robotics now has another $200 million to demonstrate that AI can do more than understand and navigate the physical world. It can help reshape it.