SoftBank backs Zurich startup Gravis Robotics with $200M to retrofit excavators for autonomous operation
SoftBank has invested $200M in ETH Zurich spinout Gravis Robotics, the largest-ever Series A in construction robotics, to scale retrofit autonomy kits across heavy equipment fleets worldwide.

Gravis Robotics AG closed a $200 million Series A round from SoftBank Group on 17 August 2026, in what the company and its backer describe as the largest funding round ever raised at Series A stage in construction robotics.[1] The Zurich-based startup, founded in 2022 as a spinout of ETH Zurich, does not manufacture heavy equipment - it makes the software and hardware that turns machines contractors already own into autonomous robots.[1]
What the Gravis system does
Rather than asking contractors to scrap their fleets, Gravis sells a retrofit approach built around two products:
- Gravis Rack - a hardware-agnostic autonomous control unit that bolts onto existing excavators and other heavy machinery. It has been installed on equipment from Caterpillar, Case, John Deere, JCB, Hitachi, Volvo, Yanmar, Sumitomo, and Develon, among others.
- Gravis Copilot - an assistance layer that keeps the operator in the cab while providing real-time 3D guidance and hazard detection.
Under full autonomous mode, operators step away from the cab and supervise multiple machines simultaneously from a safe location. In either mode, every Gravis-equipped machine acts as a continuous site sensor, automatically performing surveying and hazard mapping while carrying out its primary task. The company claims its systems deliver up to 30% improvement in jobsite productivity compared with peak manual operation, a figure drawn from its own project data. Digging currently accounts for roughly 70% of the autonomous work performed, according to CEO Ryan Luke Johns.
Valuation and funding history
SoftBank's investment values Gravis at $1 billion, making it Europe's latest robotics unicorn. The round is nearly double the amount initially reported when SoftBank was said to be considering a full acquisition of the company for less than $500 million. Gravis raised a prior $23 million round in November 2025, co-led by IQ Capital and Zacua Ventures, with Holcim, Pear VC, Sunna Ventures, and Armada Investment also participating, bringing total disclosed funding to approximately $223 million.
SoftBank managing director Dai Sakata framed the bet as part of the group's broader physical-AI strategy: "Physical AI is central to SoftBank's vision for the next phase of AI."
Current deployment and near-term targets
Gravis says its systems are already live across four continents, with customers and partners including Holcim, contractor Taylor Woodrow, and HD Hyundai. The company targets deployment in seven countries by November 2026, spanning the UK, EU, US, Latin America, and Asia.
In the UK, Gravis is leading an $8 million government-backed CAM Pathfinder project in partnership with Flannery Plant Hire, the country's largest provider of operated heavy-equipment rentals. The project involves retrofitting excavator fleets with the Gravis Rack to give contractors on-demand access to autonomous machinery.
Johns said the new capital will be used to hire engineers, accelerate international deployments, and expand the range of machine types the platform supports - with the Gravis Rack already adapted across more than a dozen brands, makes, and models, from roughly 10-tonne machines up to larger Caterpillar units.
The key question for the industry is whether Gravis can replicate controlled-site productivity numbers across the full variability of live jobsites - muddy ground, mixed crews, and shifting task sequences. SoftBank's parallel moves in industrial robotics, including its reported acquisition of ABB's robotics division, suggest the group is assembling a broader physical-AI portfolio, and Gravis's cross-manufacturer compatibility positions it as a potential platform play rather than a single-OEM bet.
Written by Construction Trade News's automated desk from the sources above and reviewed before publication. How we work.
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