Hi All, As a reminder, the final OSU Robotics seminar for the winter term will be a "fireside chat" with Dan Oblinger. He'll discuss insights from his career, which has spanned university research, work as a DARPA program manager, and founding multiple companies. The seminar will be from 10-11am, followed by an 11-11:30 student-only Q&A session. ** This seminar will be held in person in LINC 302. ** --- A simulcast of the seminar will be available via Zoom https://oregonstate.zoom.us/j/92183247338?pwd=MENOWHgvSFVGYzNjZExDT2hWRUNxdz... --- -Ross =========== Ross L. Hatton Associate Professor, Robotics and Mechanical Engineering Collaborative Robotics and Intelligent Systems Institute Oregon State University coris.oregonstate.edu research.engr.oregonstate.edu/lram/ rosslhatton.com ross.hatton@oregonstate.edu =========== Robotics Innovation In The Lab, Gov, Corp, and Startup We use the narrative stories of Bizzy, Martian, Aeolus Robotics, touch upon Dusty Robotics, Amazon's Astro and DARPA funded work, in order to talk about robotic innovation from several vantage points. BEST PRACTICES AND ANTI-PATTERNS -- We use these examples to discuss: - Founding companies, gaining product market fit - Getting funded (both governmental and investor funding) - Growing and running larger (40 PhD) R&D robotics teams. EXAMPLES OF APPLIED RESEARCH - Low cost robotics -- approaches for reducing 2 orders of magnitude in cost - Wiping Research -- Bizzy was a bathroom cleaning robot. This involved novel on robotic path planning for surface wiping. - Unconstrained Grasping -- Aeolus was a 'helper' bot, and is required to be able to manipulate found objects in an uncontrolled setting. ROBOTICS IN THIS NEW REMOTE WORLD — Not just because of covid, technical work is moving towards a planetary teams rather than co-located teams. As you can imagine this has dramatic consequences for a robotics team. I setup a 24/7 follow the sun hardware innovation lab. It kinda worked ;-). Lessons learned and thoughts for the future of robotics. DAN OBLINGER The first third of Dan's career was focused on academic research in ML and Programming By Demonstration while at IBM research and teaching in EE at Columbia University. He then served as a DARPA Program Manager funding over $200M in R&D focused on Human like learning from instruction and Language Understanding, including the work that led to the IBM Watson system beating the Jeopardy-TV-show world champion. Recently Dan has founded and exited several companies in AI, Robotics and Clean Energy.