Gravis Robotics získává $200 M v sérii A na rozšíření autonomních těžkých strojů

Gravis Robotics získala $200 milionů v sérii A od SoftBank, čímž švýcarské společnosti se sídlem v Curychu poskytla značná nová kapitálová injekce na rozšíření své technologie autonomních těžkých strojů po celosvětových infrastrukturních projektech.

Společnost popisuje toto financování jako největší sérii A v oblasti stavební robotiky doposud. Kolo přichází v době, kdy investice do physical AI se stále více rozšiřují mimo humanoidní roboty a automatizaci skladů do odvětví, kde stroje musí interagovat s komplexními, proměnlivými prostředími.

Pro Gravis je tím prostředím stavební místo. Místo toho, aby budovali nové těžké zařízení od základů, společnost vyvíjí hardware a AI software určený k retrofitování stávajících bagrů a dalších zemních strojů, čímž efektivně přidává různé stupně autonomního provozu do flotil, které dodavatelé již vlastní.

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.

Přinášení fyzické AI do těžké výstavby

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.

Stavebnictví představuje jinou výzvu než mnoho prostředí, kde autonomní stroje získaly popularitu.

Například samořiditelné vozidlo se obecně snaží pochopit a bezpečně se orientovat v prostředí, aniž by jej měnilo. Očekává se, že bagr bude dělat opak. Každý pohyb lopaty mění terén, který stroj musí následně pochopit.

Stroj může také narazit na půdu, kameny, měnící se svahy, podzemní odpor a další podmínky, které nelze předem plně předpovědět.

Gravis se snaží tuto nejistotu řešit pomocí učebních řídicích systémů, které kombinují data z hydrauliky stroje s LiDAR, kamerami a polohováním pomocí globálního navigačního satelitního systému (GNSS). Jeho software neustále interpretuje okolí stroje a fyzické síly na něj působící, místo aby jen prováděl předem určenou sekvenci pohybů.

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.

Přeměna stávajících bagrů na autonomní stroje

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

Stavební flotily jsou typicky složeny ze strojů od více značek, často nahromaděných během let nákupů a pronájmů. Požadovat od dodavatelů výměnu těchto flotil za speciálně vyrobené autonomní stroje by mohlo adopci výrazně ztížit.

Namísto toho Gravis vyvinula Gravis RACK, modulární autonomní řídicí systém, který lze nainstalovat na existující bagry a nakladače. Společnost uvádí, že platforma již byla přizpůsobena zařízení od výrobců včetně Caterpillar, John Deere, JCB, Hitachi, Volvo, Yanmar, Case, Develon a Sumitomo.

Střešní systém kombinuje kamery, 3D LiDAR, GNSS RTK polohování a výpočetní výkon automobilové úrovně na okraji. Protože zpracování probíhá přímo na stroji, Gravis tvrdí, že autonomní funkce mohou nadále fungovat i při nedostupnosti spolehlivého připojení, což je důležité na odlehlých nebo nedokončených stavebních místech.

Kompletní pokrytí LiDAR a sloučení senzorů se používá k neustálému skenování okolního terénu. Tyto informace mohou podporovat autonomní výkopové práce a zároveň vytvářet 3D data o staveništi, vizualizace výkopu a výplně a záznamy o dokončené práci.

Gravis’ perception system can also identify dump trucks and coordinate where excavated material should be placed, allowing autonomous workflows to extend beyond digging into activities such as loading and material handling.

Autonomie bez odebrání operátora

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.

Softwarová vrstva napříč smíšenými stavebními flotilami

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.

Přechod od výzkumných projektů k aktivním staveništím

Gravis had already begun expanding commercially before the SoftBank investment.

In listopad 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.

Projekt ve Velké Británii zařadí autonomní bagry do většího testu

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.

Proč se výstavba stává významným trhem pro fyzickou 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.

Financování SoftBanku uvádí Gravis do nové fáze expanze

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.