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Temasek-backed robotics firm Rhoda AI raises $450m series A
Rhoda AI, Palo Alto startup building robot foundation models, exited stealth and raised US$450 million in a series A to launch FutureVision, an intelligence layer based on video‑predictive control.
It says the models combine internet-scale video pretraining with robot-specific post-training to predict future frames and map them to actions in a closed loop.
The company said its direct video action model often requires as little as ten hours of teleoperation data.
The system completed a manufacturing component-processing workflow in under two minutes per cycle without human intervention, exceeding customer KPIs.
Rhoda said the series A will fund research, industrial deployments, and customer pilots and that investors include Capricorn Investment Group, Khosla Ventures, Temasek, Leitmotif, and Premji Invest, among others.
Rhoda is led by Jagdeep Singh, co-founder and CEO, and said its senior team includes Stanford researchers and engineers from generative AI and robotics organisations.
🔗 Source: Rhoda AI
🧠 Food for thought
Implications, context, and why it matters.
Rhoda’s funding backs closed-loop video predictive control
- Rhoda’s Direct Video Action (DVA) model is pre-trained on internet-scale video, “hundreds of millions of videos,” to build a strong prior on motion, physics, and physical interaction, then post-trained on smaller amounts of robot data 1.
- The goal is to close a gap where newer AI approaches, especially vision-language-action (VLA) models that combine visual inputs, natural-language instructions, and action outputs, can perform well in labs yet struggle with shifting layouts, unseen objects, and unpredictable workflows 1.
- Investors say better handling of real-world variability could broaden what gets automated and support “re-industrializing mature economies” 1.
A $450 million sign that investors expect a robotics data flywheel
- Rhoda’s $450 million Series A included investors such as Khosla Ventures and Temasek 1.
- Premji Invest argued that scaling intelligent, manipulation-capable robots in real workplaces could “kick start a powerful data flywheel,” since deployment can capture the long tail of edge cases and build a compounding advantage 1.
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