OpenAI Ramps Up Robotics Work in Race Toward AGI (www.wired.com)

OpenAI has quietly ramped up its robotics push, hiring multiple researchers with humanoid-robot expertise and posting jobs that signal a focus on training robots via teleoperation and simulation. Recent hires include Chengshu Li (formerly at Stanford) and others from leading robotics labs; listings call for teleoperation know-how, experience with Nvidia Isaac simulation, and mechanical engineers who can prototype sensor-laden systems and design for high-volume production. Roles repeatedly state the team’s mission: “unlocking general-purpose robotics and pushing towards AGI-level intelligence in dynamic, real-world settings,” though OpenAI hasn’t commented publicly on plans or whether it will build hardware, use partners, or mass-produce units. This matters because it signals OpenAI’s bet that AGI will require embodied agents that learn from high-frame-rate, high-dimensional perceptual inputs and produce high-fidelity physical outputs—capacities beyond today’s LLM-centered toolset. Technically, the emphasis on teleoperation and simulated training echoes successful pipelines for learning manipulation and locomotion, while hires for touch/motion sensing and manufacturable designs imply ambitions from lab prototypes to scalable systems. The move reintroduces OpenAI to an increasingly crowded humanoid field (startups like Figure, Agility, Apptronik and incumbents including Tesla and Google) and highlights key challenges: integrating perception, control, and simulation to operate in unstructured environments—an arena many see as essential for true AGI.