Design and implement advanced control algorithms for bipedal locomotion and manipulation. Train reinforcement learning policies in simulation and deploy them to physical robots. Perform sim-to-real transfer and analyze gait and kinematics telemetry to improve performance.
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:
- Develop and implement advanced control algorithms for bipedal locomotion and manipulation.
- Train Reinforcement Learning (RL) policies in simulation and deploy them on physical hardware.
- Conduct sim-to-real transfer and analyze gait/kinematics telemetry.
Required Skills:
- Strong background in Model Predictive Control (MPC) and Whole-Body Control.
- Extensive experience with robotic simulation environments (e.g., NVIDIA Isaac Sim, MuJoCo).
- Familiarity with modern RL frameworks.
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