Edinburgh start-up Danu Robotics has raised $5 million to bring H.E.R.O., a pincer-claw recycling robot with self-improving AI, to sorting sites. We explain how it works, work out the payback on Danu’s own figures, compare its speed and funding with AMP, Glacier and Recycleye, and look at the UK and EU rules driving demand.
Safeworld, co-founded by Carnegie Mellon’s Ding Zhao, has raised $12.2 million to test AI-controlled robots in simulations full of realistic digital humans. We explain how its tests work, what solar-farm robot maker Gritt wants from them, the case for and against a third-party tester, and the standards and EU rules now arriving.
Destro AI has raised an $8 million seed round led by Base10 Partners and Bonfire Ventures for software that directs warehouse robots, carts, trucks and people as one system. We explain MothershipOS and VisionOS, the Yusen Logistics cross-dock deployment growing from 3 robots to 43, why orchestration may matter more than better robots, two corrections to the coverage and the risks in the model.
Robot fleet management software turns individual robots into a coordinated system. This guide explains what it does (task allocation, traffic control, charging, maps, monitoring and updates), the three kinds of platform, the VDA 5050 3.0, Open-RMF and MassRobotics interoperability standards, architecture and WMS integration, safety rules including ISO 3691-4 and the EU Machinery Regulation, the KPIs to track, a worked fleet-sizing example, illustrative three-year costs and how to choose a platform.
Shield AI CTO Nathan Michael, Waabi CEO Raquel Urtasun and GM robotics strategy director Mikell Taylor share the Real World AI Stage at TechCrunch Disrupt 2026 (13-15 October, Moscone West) for “Building AI Systems When Failure Is Not an Option”. This article covers the session, each company’s record on validation, RAND’s miles-to-safety arithmetic, the standards behind a release decision and the questions founders should ask about safety-critical AI.
A paper accepted to ECCV 2026 by researchers at DGIST and KAIST identifies Skill Conflict, a phenomenon in which motion planning and motion prediction compete for the same weights inside the compact shared encoders that mobile robots run. Their fix, Disjoint Parameter Training with Sparse Merging, cut the JRDB collision rate from 0.0189 to 0.0091 on a 6.44 million parameter model. We read the paper, the supplementary tables and the ablations, including the three-way task split that made results worse.
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