Google parent company Alphabet has announced the launch of a new robotics software company called Intrinsic, spun out of its X research lab, or so-called ‘moonshot factory’. The X division has previously birthed standalone Alphabet businesses including autonomous car company Waymo and life sciences outfit Verily.
Intrinsic is focused on developing software tools to make industrial robots “easier to use, less costly, and more flexible”, according to a blog post by Wendy Tan-White, appointed chief executive at the firm. The post said training robots to complete tasks is labour intensive, requiring specialist engineers, and a blocker to enterprise productivity, and economic progress.
The effect is to hinder local production, and deepen the world’s reliance on international manufacturing strongholds — in turn driving inefficient, costly, and environmentally damaging import/export markets. Tan-White quoted the World Economic Forum, that 70 percent of the world’s goods are made in 10 countries, and the International Transport Forum, that 30 percent of all transport-related CO2 emissions are from freight.
At the same time, countries with strong manufacturing sectors are struggling to fill jobs, with 2.1 million projected to be unfilled in the US by 2030, according to Deloitte, also quoted. The mission statement for the Intrinsic team, as a new money-making part of the $182 billion Alphabet group, is to bring manufacturing home, closer to customers, by making industrial robots easier to manage and more productive.
Tan-White wrote: “By unlocking access to these incredible productivity tools, we hope to support a shift towards a more sustainable and equitable way of making things…. The manual and bespoke process of teaching robots how to do things, which hasn’t changed much over the last few decades, is currently a cap on their potential to help more businesses.
“Over the last few years, our team has been exploring how to give industrial robots the ability to sense, learn, and automatically make adjustments as they’re completing tasks, so they work in a wider range of settings and applications. Working in collaboration with teams across Alphabet, and with our partners in real-world manufacturing settings, we’ve been testing software that uses techniques like automated perception, deep learning, reinforcement learning, motion planning, simulation, and force control.”
