Xiaolong Wang, who co-founded ARI with Lerrel Pinto, shared news of the acquisition in a Friday (May 1) post on X.
“Excited to share that Assured Robot Intelligence (ARI) has joined [Meta] to help build the future of humanoid intelligence,” Wang said in the post.
Meta Chief AI Officer Alexandr Wang reposted this message, adding: “Welcome Assured Robot Intelligence (ARI) to MSL [Meta Superintelligence Labs]! Excited to build physical with [Pinto and Wang] and the whole team!”
Xiaolong Wang shared in his post a link to a report by Bloomberg, which said that ARI is working on technology that enables robots to understand human behaviors in dynamic environments. The report said that Meta plans to use ARI’s technology alongside its own software and hardware for robots.
In his LinkedIn post, Xiaolong Wang said ARI was launched one year ago to achieve physical artificial general intelligence (AGI) and found that it will take a general-purpose physical agent to meet the opportunity available in this field.
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“We believe this agent will be humanoid — and that scaling will come from learning directly from human experience, not teleoperation alone,” Wang said. “Meta’s ecosystem brings together the key components needed to make this vision possible. We will be joining Meta Superintelligence Labs (MSL) to help bring personal superintelligence into the physical world.”
It was reported in February 2025 that Meta aimed to develop AI-powered humanoid robots. The report added that the company planned to work on its own humanoid robot hardware, with a focus on robots that can perform household chores, as well as AI, sensors and software that could be used by a variety of companies manufacturing and selling robots.
Last June, Meta introduced a model to help train robots and other AI agents, saying the model is designed to help these agents “understand the physical world and predict how it will respond to their actions.”
PYMNTS reported in November that physical AI is emerging as the next stage of robotics as advances in sensing, perception and large AI models give machines capabilities that traditional automation never supported.
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