Robotics
3 items across 3 editions. First seen Fri 11 Sep, last seen Tue 15 Sep.
- Air Force Secretary Troy Meink said at AFA's Air, Space & Cyber Conference in National Harbor, Maryland: "We intend to have at least 500 of these in service by 2032, and they'll be performing many of the same missions that we do with manned fighters today." DefenseScoop published the account on 14 September.
- DefenseScoop reports General Atomics' aircraft is designated FQ-42A Vengeance and Anduril's is FQ-44A Fury, that both manufacturers have begun producing Increment 1 aircraft, and that the Air Force requested $996.5 million in fiscal 2027 to start Increment 1 procurement. It puts the cost at roughly $30 million per aircraft, about a third of an F-35.
- Alongside the CCA programme, DefenseScoop reports a Massed Modular Aircraft line targeting 100 platforms by 2029 and 500 in service by 2032, and a Family of Affordable Mass Munitions with nearly 28,000 units planned over five years and production beginning in autumn 2026.
- Neither the mission autonomy software nor the rules of engagement for these aircraft were detailed at the conference, and no test results or autonomy evaluation data were released alongside the numbers.
- TechCrunch reported on 11 September at 3:58 pm PDT that Mecka AI is nearing a Sequoia Capital-led round at a valuation of about $500 million; the round size is not disclosed and "terms of the deal are not final and could still change".
- That follows a $60 million Series A announced three months earlier, led by Framework Ventures with Menlo Ventures, SV Angel and Kindred Ventures.
- Mecka pays people to record themselves performing everyday tasks using body sensors and smartphones, and sells that motion data to train humanoid robots. It was founded in 2024 by Josh Gao, Mogen Cheng, Jason Chong and Duy Nguyen, and as of early June was projecting an annual run rate of $100 million by the end of 2026.
- TechCrunch names competitor XDOF as nearing a $1.2 billion valuation. The valuation and run-rate figures come from sources and company projections rather than filings.
- Announced 10 September: Skild AI's S1 learns new manipulation tasks from a single video demonstration using in-context learning, with no weight updates or task-specific retraining. NVIDIA reports roughly 66% per-step success on multistep tasks against about 9% for comparable systems, and that one video example is worth roughly 380 hands-on training examples — 50 to 100 hours of manual collection.
- S1 executes unfamiliar tasks up to 10 minutes long across dozens of steps, including potting plants, making pancakes, pour-over coffee and kit assembly. In one plant-potting test, the gap from recording the demonstration to autonomous execution on hardware was 11 minutes.
- Skild reports a $100 million annual revenue run rate ten months after launch and more than 60 deployment partnerships across manufacturing, logistics, inspection, security and food preparation. If the demonstration-efficiency claim holds outside curated tasks, the cost of teaching a robot a new job falls by orders of magnitude — which is the labour-substitution variable to watch.
- These are vendor figures published on a supplier's blog, not an independent benchmark. "Per-step" success is not end-to-end task success, and the 66% versus 9% comparison does not name the baseline systems.