Skild AI just applied AlphaGo-style self-play to humanoid robots using its S1 model. Trained inside NVIDIA's Isaac Sim with the single goal of scoring, the system simulated 140 years of matches to master dribbling and tackling before successfully transferring into a physical robot.
Skild AI just launched S1, a robotics foundation model that learns unseen, 10-minute tasks from a single video prompt without any fine-tuning. Using in-context learning similar to LLMs, it matches conventional VLA models that typically require 50 to 100 hours of training data. It is currently being rolled out to select industrial partners.
AI researchers at Stanford and the University of Washington just created an open-source reasoning model called s1 for under $50 in cloud compute. By distilling thinking steps from Google's Gemini into a Qwen base model using 1,000 curated questions, s1 matches frontier reasoning models like OpenAI's o1 on math and coding tests.
