Startup Fundraising

Gravis Robotics Hits $1B Valuation with $200M Funding

Swiss startup Gravis Robotics secures $200M Series A from SoftBank, reaching unicorn status with its autonomous excavator retrofit technology.

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Alvaro de la Maza

Partner at Aninver

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Key Takeaways

  • Gravis Robotics raised $200.0M (Series A) from SoftBank Group, IQ Capital, Zacua Ventures, Pear VC, Sunna Ventures, Armada Investment, Holcim.
  • Sector: Industrials, Technology, Software & Gaming.
  • Geography: Switzerland, Japan, United States, United Kingdom.

Analysis

Swiss robotics innovator Gravis Robotics has officially joined the unicorn club, achieving a valuation of approximately $1 billion post-money following a substantial $200 million Series A funding round. The entire investment was led by the prominent Japanese conglomerate SoftBank Group, marking a significant endorsement of the company's vision in the construction automation sector. This funding represents one of the largest Series A rounds ever seen in construction robotics, underscoring the immense potential investors see in automating heavy machinery.

Previously, Gravis Robotics had secured smaller tranches of capital, including a $23 million round in November led by IQ Capital and Zacua Ventures, with participation from Pear VC, Sunna Ventures, Armada Investment, and construction materials giant Holcim. The recent influx of capital from SoftBank signals a strategic shift for the investor towards 'Physical AI' and advanced robotics, a trend also evidenced by its acquisition of ABB's industrial robotics division and its planned public listing of a new AI and robotics entity, Roze.

Founded in 2022 as a spin-off from ETH Zurich's Robotic Systems Lab, Gravis Robotics, co-led by CEO Ryan Luke Johns and CTO Dominic Jud, with ETH robotics professor Marco Hutter as a co-founder and board member, is pioneering a unique approach. Instead of developing entirely new autonomous construction equipment, the company focuses on retrofitting existing excavators and other earthmoving machinery. Their core product, the Gravis Rack, is a sophisticated kit comprising sensors, computing hardware, and proprietary software designed to be installed on machines from major manufacturers like Caterpillar, Case, Develon, John Deere, JCB, Hitachi, Sumitomo, Yanmar, and Volvo, across a range of sizes.

This retrofit strategy addresses the fragmented nature of the construction equipment market, where the top three manufacturers do not capture the majority of global demand. By enabling companies to upgrade their current fleets, Gravis Robotics avoids locking customers into proprietary ecosystems. The company's technology leverages simulation-trained models adapted for real-world construction sites. Unlike autonomous vehicles that navigate static environments, construction equipment actively modifies its surroundings, requiring sophisticated software that analyzes machine telemetry to adapt to changing ground conditions and hydraulic resistance, as highlighted by CTO Dominic Jud.

The Gravis software offers versatile operational modes, ranging from a 'copilot' system that provides 3D guidance and hazard detection to the operator within the cabin, to fully autonomous operation where a single supervisor can manage multiple machines. The system is capable of automating tasks such as driving, trenching, bulk excavation, truck loading, and stockpile management. Gravis Robotics claims its solution can boost productivity by up to 30% compared to manual operations, a figure derived from their internal project data.

With systems already deployed across four continents and partnerships with entities like Holcim, construction firm Taylor Woodrow, and HD Hyundai, Gravis Robotics is demonstrating significant market traction. The company is also leading a $8 million government-backed CAM Pathfinder project in the UK with Flannery Plant Hire, involving the retrofitting of six excavators. The newly acquired capital will fuel team expansion, market penetration into new territories, and an increase in the number of commercially deployed autonomous machines.