Raise the quality of the overall controls stack — architecture, algorithms, real-time performance
Advance whole-body control across platforms — wheeled-base and biped, coordinating base/legs with
Flex across the stack to high-impact problems — learning-based hand control (RL in simulation)
Translate state-of-the-art model-based and learning-based methods into robust capability from
Expand the robot's performance envelope — speed, payload, reliability, safety with measurable
Set technical direction across the controls stack, influence roadmap, and align adjacent teams

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