# Skild AI trains robot to play football using 140 years of simulated self-play > Source: > Published: 2026-09-23 17:37:05+00:00 Photo: Tima Miroshnichenko / Pexels # Skild AI trains robot to play football using 140 years of simulated self-play The robotics startup's S1 model learned to score goals by competing against itself in NVIDIA Isaac Sim, then transferred those skills to the real world without any task-specific demonstrations. Skild AI revealed on September 22 that its S1 model developed soccer-playing capabilities through a self-play training method conducted entirely within [NVIDIA](https://cryptobriefing.com/markets/nvidia/) Isaac Sim. The training was focused on a single objective: score goals. No tailored rewards, no task-specific demonstrations, no hand-holding from human trainers. The resulting policy transferred directly to real-world scenarios, where the robot can now play football against both humans and other robots. ## How 140 years fits inside a few weeks Modern GPU clusters can run thousands of parallel simulated environments simultaneously, compressing what would be over a century of real-time experience into a fraction of that in wall-clock time. Self-play, the technique Skild used, has a proven pedigree. DeepMind famously used it to create AlphaGo and AlphaZero, systems that mastered board games by competing against copies of themselves. The twist here is that Skild applied this approach not to a board game with discrete moves, but to the messy physics of a bipedal robot navigating a football pitch, involving continuous motor control, balance, object tracking, and real-time decision making. The robot’s training objective was stripped down to its essence. Score the goal. Everything else, the footwork, the positioning, the ball control, emerged as learned behaviors rather than programmed ones. ## The S1 model and Skild’s broader ambitions Football is the flashy demo, but the S1 model’s real commercial value lies in its versatility. Launched in late August 2026, the S1 can perform complex manipulation tasks lasting up to 10 minutes from a single video demonstration. No fine-tuning, no parameter updates. The company has demonstrated the S1 performing tasks ranging from pancake flipping to kit assembly. ### AI, tech, and the markets they move—in one daily briefing. Daily. Free. Join 34,000+ readers across crypto, finance, and policy. Skild AI’s development philosophy draws explicit parallels to large language models, training on extensive data from human videos and physics simulations to build general-purpose motor intelligence. NVIDIA’s simulation infrastructure plays a critical role in this strategy. Isaac Sim provides the physics engine and rendering pipeline that makes high-fidelity robotic training feasible at scale, helping compress the sim-to-real gap. ## A $14 billion bet on general-purpose robots Skild reached a $100 million annual recurring revenue run rate shortly after its first commercial deployment earlier in 2026. Its robots currently operate across more than 60 client companies. Skild raised $1.4 billion in a Series C funding round in January 2026, achieving a valuation exceeding $14 billion. SoftBank and NVIDIA participated in the round. ABB Robotics and Teradyne, which owns Universal Robots and MiR, are both working with Skild. Skild has also deployed robots assembling NVIDIA Blackwell GPU systems at Foxconn. **Disclosure:** This article was edited by Editorial Team. For more information on how we create and review content, see our [Editorial Policy](https://cryptobriefing.com/editorial-policy/).