Edge Research: Working Out What’s Next. Keep up with what’s new in robot learning and work out what it means for the companies we back: VLAs, world models and WAMs, sim-to-real tooling and how to evaluate any of it properly. What you research turns into practical papers, tools, write-ups and advice.
Portfolio Acceleration: Hands-On Impact. Once we’ve invested, you’ll work inside portfolio companies to get robot-learning software from pilot into production: perception, RL policy training and fine-tuning, imitation learning, VLAs/WAMs, world models and simulation, data pipelines, evals and the on-robot inference and fleet tooling that keeps it all working in the field. You’ll work with CTOs and heads of autonomy on results they can measure: task success rate, how often someone has to intervene and how long it takes to stand up a new site.
Find & Win: Technical Due Diligence & Deal Flow. You’ll work with our investment team to look hard at physical AI companies, digging into each company’s autonomy stack, data and roadmap to answer the questions that matter. Is the autonomy learned or scripted? How reliably does it complete the task in messy, real-world conditions, and how often does a person have to step in once you’re past the demo? How long does it take to get a robot working at a new site or on a new task, and does each deployment produce data that makes the next one faster, or is every site a one-off integration?
What’s hard to copy: the data, the models, the integration work or the hardware? What you find informs our view of the company, and you’ll talk directly with founders about what our AI Lab could build with them.