According to @berkeley_ai referencing @Ken_Goldberg's editorial in @SciRobotics, leveraging established engineering principles alongside modern AI techniques can effectively address the vast 100,000-year 'data gap' in robotics. Goldberg argues that by applying good old-fashioned engineering methods—such as simulation, modular design, and robust mechanical architectures—robotics researchers can accelerate data collection, improve reliability, and enable practical deployment of autonomous systems. This approach highlights a significant business opportunity for companies to integrate traditional engineering with AI-driven robotics to expedite product development, reduce costs, and enhance real-world performance. The editorial underscores the importance of multidisciplinary teams and signals a trend toward hybrid solutions to close critical data deficits in the robotics industry (Source: SciRobotics editorial by Ken Goldberg, August 2025).

According to Google DeepMind (@GoogleDeepMind), the company has unveiled Gemini Robotics On-Device, its first vision-language-action model designed to run directly on robots without requiring a constant internet connection. This new AI system enables robots to process visual, linguistic, and action cues locally, making them faster, more efficient, and adaptable to dynamic environments and new tasks. The on-device capability addresses challenges of latency and connectivity, unlocking business opportunities in sectors like manufacturing, logistics, and healthcare where reliable offline performance is critical. The advancement positions Google DeepMind at the forefront of embedded AI robotics, with the potential to accelerate the deployment of autonomous systems across various industries (source: Google DeepMind, June 24, 2025).