We are searching for a Humanoid Robotic Engineer specialized in bipedal locomotion and manipulation. This role involves designing and implementing sophisticated control algorithms, with a focus on Reinforcement Learning, to enhance the performance of robotic systems both in simulation and real-world applications.
Responsibilities:
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Develop and implement advanced control algorithms for bipedal locomotion and manipulation.
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Train Reinforcement Learning (RL) policies in simulation and deploy them on physical hardware.
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Conduct sim-to-real transfer and analyze gait/kinematics telemetry.
Required Skills:
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Strong background in Model Predictive Control (MPC) and Whole-Body Control.
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Extensive experience with robotic simulation environments (e.g., NVIDIA Isaac Sim, MuJoCo).