Kyle Morgenstein is a reinforcement learning and robotics researcher pursuing a PhD at UT Austin and leading RL efforts at Apptronik to enable safe, perceptive locomanipulation, motion imitation, and human-robot physical interaction. He blends academic rigor—work on safety, explainability, and pro-social behaviors for humanoids—with industry impact from internships at the Boston Dynamics AI Institute and multiple roles at NASA JPL and Boeing. Trained at MIT in aerospace and planetary science, he brings uncommon cross-disciplinary depth, from infrared interferometer dynamics to microbe–mineral geobiology. Kyle’s practical toolkit spans control, computer vision, and ML, and he has a track record of shipping simulation and autonomy tools for space and legged robots. He is focused on building “friendly” robots with provable safety properties and explainable behavior, bridging theory and deployable systems.
8 years of coding experience
4 years of employment as a software developer
Bachelor of Science - BS, Double Major: Aerospace Engineering and Earth Atmospheric & Planetary Science (EAPS), Bachelor of Science - BS, Double Major: Aerospace Engineering and Earth Atmospheric & Planetary Science (EAPS) at Massachusetts Institute of Technology
Doctor of Philosophy - PhD, Robotics, Doctor of Philosophy - PhD, Robotics at The University of Texas at Austin
High School, Science, A, High School, Science, A at Cary High School
Contributions:23 commits, 2 PRs, 20 pushes in 1 day
bodyn-bodysimulationastrodynamicssimulator
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Kyle Morgenstein - Reinforcement Learning at Apptronik