Skild AI Secures $1.4B Funding to Build a General‑Purpose Robot Brain
Skild AI raised about $1.4 billion in a C‑round led by SoftBank, with participation from Nvidia, Sequoia, Bezos Expeditions and others, to develop a universal foundation model—Skild Brain—that can be deployed across diverse robot platforms, leveraging large‑scale visual data and a hierarchical control architecture.
Any robot. Any task. One brain.
Skild AI’s slogan "Any robot. Any task. One brain" encapsulates its ambition to create a single, universal intelligence that can power any type of robot, from humanoids to quadrupeds and industrial arms.
Two “mentor” founders: from academic research to industry
The company was founded by Deepak Pathak and Abhinav Gupta, both veteran AI and robotics researchers formerly at Carnegie Mellon University. Pathak, now CEO, previously authored the 2017 paper "Curiosity‑driven Exploration by Self‑supervised Prediction," cited nearly 4,000 times (Forbes). Gupta, president, specializes in large‑scale learning from massive unlabeled video datasets, bringing the ability to endow robots with common‑sense knowledge through visual observation.
In 2023 they launched Skild AI not to chase quick profits but to translate years of academic work into a commercial system that overcomes the vertical, task‑specific fragmentation of traditional robotics. Their vision attracted experts from Meta, Tesla, Nvidia, Amazon, Google and leading universities such as CMU, Stanford, UC Berkeley and UIUC.
Skild Brain brings a foundation model into the physical world
Skild Brain is positioned as a deployable, general‑purpose intelligence rather than a task‑specific controller. It follows a hierarchical architecture: a low‑frequency high‑level planner interprets semantic environment information and sets goals, while a high‑frequency low‑level controller executes motions end‑to‑end using online visual and proprioceptive feedback, forming a closed‑loop physical interaction.
Omni‑bodied cross‑form capability: The same pretrained model can drive quadruped robots, bipedal robots and robotic arms, having learned universal physical laws from diverse robot‑form data.
Learning by watching: Skild AI bypasses costly human demonstrations by ingesting billions of human‑activity videos from the internet, converting visual signals into robotic experience and enabling strong zero‑shot generalization.
One policy, all scenarios: Real‑world tests show the system remains robust across smooth lab floors, cluttered warehouses, and rugged outdoor terrains, adapting posture in real time with a single policy.
These capabilities aim to give robots the physical reasoning and adaptability needed to move beyond controlled labs into varied real‑world applications.
Conclusion
Skild AI has chosen a high‑risk path by betting on universal robot intelligence at a time when hardware standards are still fluid and application boundaries are fragmented. While the ultimate success of a truly general robot brain remains to be proven, the shift of capital, research talent, and startups toward this foundational problem signals a broader industry pivot.
References:
1. Bloomberg. https://www.bloomberg.com/news/articles/2026-01-14/robotics-startup-skild-valued-above-14-billion-after-softbank-led-funding-round
2. Forbes. https://www.forbes.com/sites/rashishrivastava/2024/07/09/this-15-billion-ai-company-is-building-a-general-purpose-brain-for-robots
3. Business Wire. https://www.businesswire.com/news/home/20240709306400/en/Skild-AI-Raises-%24300M-Series-A-To-Build-A-Scalable-AI-Foundation-Model-For-Robotics
4. YouTube. https://www.youtube.com/watch?v=yesita2zN5c
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