Ted Larson is CEO and Co-Founder of OLogic, an embedded systems research and development company with a focus on robotic applications and technologies. Ted has over 25 years of experience designing robots and consumer electronics. His current interest areas are robotics, artificial intelligence, and embedded systems in consumer and professional electronic devices.
Ted graduated from California Polytechnic State University with B.S. and M.S. degrees in Computer Science. He has worked on numerous robotics projects with leading corporations including Google, Hasbro, Savioke and Panasonic. Ted has spoken at several of the past RoboBusiness events, the annual Silicon Valley conference that has helped business leaders understand where the market is going, and which has assisted in implementing their robotics vision within their organizations.
Egocentric robot learning is emerging as a powerful approach for developing more capable, adaptable, and scalable robotic systems. By learning from first-person human demonstrations captured through wearable cameras, teleoperation systems, and embodied AI platforms, robots can acquire complex manipulation and decision-making skills in ways that more closely mirror human learning. This paradigm has gained significant momentum through advances from organizations such as NVIDIA, Google DeepMind, Meta, and a growing ecosystem of robotics startups focused on foundation models, imitation learning, and embodied intelligence. As labor shortages, increasing customization demands, and operational complexity continue to challenge industries, egocentric learning offers a path to reducing the time, cost, and expertise traditionally required to deploy robotic systems. By enabling robots to learn directly from human behavior, organizations can accelerate automation across manufacturing, logistics, healthcare, service robotics, and other commercial applications. In this session, Ted Larson, CEO of OLogic, will explore the foundations of egocentric robot learning, the technical challenges involved in building these systems, and their implications for the future of commercial robotics. Attendees will gain insight into how advances in embodied AI, multimodal data collection, and large-scale robot learning are reshaping robot development and deployment. The presentation will examine both the opportunities and challenges ahead, highlighting how egocentric learning can help bridge the gap between research innovation and scalable, real-world robotic applications.
