Overview
<cite index="16-2">Rhoda AI announced its public launch on March 10, 2026, after 18 months in stealth, unveiling FutureVision, a new approach to robotic intelligence based on video-predictive control and designed to operate beyond controlled laboratory demonstrations and into real-world environments.</cite> <cite index="16-3">The company also announced it has raised $450 million in Series A funding to accelerate development and industrial deployment.</cite>
<cite index="6-3">Premji Invest led the Series A round, bringing the valuation of the startup to $1.7 billion.</cite> <cite index="4-7">The company is backed by top technology investors, including Capricorn Investment Group, Khosla Ventures, Leitmotif, Matter Venture Partners, Mayfield, Premji Invest, Prelude Ventures, Temasek, and Xora, as well as Silicon Valley leaders</cite> <cite index="3-6">including technology investor John Doerr.</cite>
Technology
<cite index="16-4,16-5">Traditional industrial robots perform well in structured environments but remain largely limited to pre-programmed trajectories. More recent AI approaches — particularly vision-language-action (VLA) models — allow robots to learn from data and have demonstrated impressive results in laboratory settings.</cite> <cite index="22-6">However, many still struggle to cope with the variability of the real world, including shifting layouts, previously unseen objects, and unpredictable workflows.</cite>
Rhoda's platform addresses this gap through a novel training methodology. <cite index="2-9,2-10">Rather than relying primarily on teleoperated robot trajectories, Rhoda pre-trains its models on internet-scale video — hundreds of millions of videos — to build a strong prior on motion, physics, and physical interaction. The company then post-trains the models on smaller amounts of robot data to learn embodiment-specific behaviors and the mapping from video predictions to robot actions.</cite>
<cite index="17-1">Rhoda calls this architecture a Direct Video Action (DVA) model that links perception and control by continuously updating robot actions as conditions change, enabling real-time, physics-aware operation.</cite> <cite index="18-9,18-10">The system continuously observes its environment, predicts future states using video-based modeling, and converts those predictions into actions. The process runs in a closed feedback loop, updating robot behavior every few hundred milliseconds as conditions change.</cite>
<cite index="20-4">The strong motion prior learned during Rhoda's natively autoregressive video-based pretraining allows the model to learn new tasks efficiently, often requiring as little as ten hours of teleoperation data.</cite>
<cite index="22-7">Built on this architecture, FutureVision serves as Rhoda's intelligence layer — a foundation model that powers Rhoda systems today and is expected over time to be licensed to partners across different robotic hardware and software platforms.</cite>
Use Cases and Early Deployments
<cite index="8-7">The company works with industrial partners across manufacturing and logistics.</cite> <cite index="23-3">In one high-volume manufacturing evaluation, Rhoda reported that a robot system completed a component-processing workflow in under two minutes per cycle without human intervention, exceeding customer performance targets.</cite>
Leadership
<cite index="24-6">Rhoda is led by CEO and cofounder Jagdeep Singh, a serial deep-tech founder who has built and scaled multiple technology companies; Chief Science Officer Eric Ryan Chan, a Stanford researcher and leader in computer vision and generative modeling who previously served as a generative model architect at WorldLabs; Gordon Wetzstein, professor at Stanford University and head of the Computational Imaging Lab; and a team drawn from leading generative AI, computer vision, and robotics organizations.</cite>
Use of Proceeds
<cite index="16-1">The $450 million Series A will support continued research and engineering investment, expansion of industrial deployments and customer pilots, and growth of Rhoda's multidisciplinary team spanning generative AI, computer vision, and robotics.</cite>
<cite index="12-8">The funding marks one of the largest early-stage investments in the robotics and physical AI sector, reflecting growing investor interest in technologies that can bring general-purpose intelligence to machines operating in the physical world.</cite>