Sertac Karaman
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- Lex Fridman1h 23m
Sertac Karaman: Robots That Fly and Robots That Drive | Lex Fridman Podcast #97
Experts project that mass-deploying autonomous flying vehicles will prove more difficult than autonomous driving due to the complexities of navigating dense, human-centric environments without prior training data. Current development strategies contrast Waymo's long-term AI research with Tesla's data-driven market approach, while companies like Optimus Ride focus on geofenced, small-vehicle fleets to bypass traditional transit inefficiencies. Despite significant hurdles in simulating realistic human behavior and achieving high-frequency sensor processing, iterative testing in crash-tolerant environments like the AlphaPilot drone racing challenge aims to bridge the gap toward practical, Level 5 autonomy.
- Lex Fridman1h 2m
Sertac Karaman (MIT) on Motion Planning in a Complex World - MIT Self-Driving Cars
Sertac Karaman, Sirtesh Karaman, Lex
MIT AeroAstro professor Sirtesh Karaman discusses his pioneering RRT* algorithm, which guarantees optimal trajectory convergence for autonomous vehicles, and reflects on MIT's 2007 DARPA Urban Challenge success where his team developed software now standard in the automotive industry. Karaman outlines current research into ultra-agile robotics and compressed high-dimensional control systems, while detailing the commercial launch of Optimus Ride and projecting the near-term viability of vision-only autonomy and vehicle-to-infrastructure communication networks. The presentation concludes by analyzing how formal optimization and deep learning will transform logistics costs and overcome non-technical regulatory barriers in the evolving landscape of self-driving technology.