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Barrett Ames is a leading figure in humanoid robotics and robot autonomy, best known for his foundational work on NASA’s Valkyrie robot. As a core contributor to the 2015 paper "Valkyrie: NASA's First Bipedal Humanoid Robot" (298 citations), Ames helped design and deploy one of the most advanced humanoid platforms ever built, which competed in the DARPA Robotics Challenge—a competition directly inspired by the Fukushima Daiichi disaster. This work established him as a key architect of disaster-response capable humanoids. Ames has since pushed the boundaries of robot motion planning and skill learning. His 2022 paper "IKFlow: Generating Diverse Inverse Kinematics Solutions" (46 citations) introduced a novel, learning-based approach to a classic robotics problem, enabling robots to find a wide range of joint configurations for a given end-effector pose—a critical capability for manipulation in cluttered environments. Earlier, in "Learning Symbolic Representations for Planning with Parameterized Skills" (2018, 25 citations), he tackled the challenge of enabling robots to autonomously sequence motor skills to achieve complex goals, bridging the gap between low-level control and high-level planning. With over 350 total citations, Ames’s work is essential reading for anyone interested in humanoid robotics, inverse kinematics, and robot learning for autonomous planning.