Dynamics, System Identification & Estimation Engineer - PLA (human)

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From Neura Robotics’s original posting

Your Mission & Challenges • Dynamic model accuracy ownership: defining model fidelity metrics and owning the gap between simulation behaviour and real-hardware behaviour across dynamic motion and contact-rich interactions • System identification on 4NE1 hardware: motor constants, joint friction, transmission dynamics — excitation trajectory design, regressor fitting, observability analysis, iterative refinement against hardware data • Simulation model authoring and maintenance: MuJoCo and Isaac Sim models that match real-world behaviour under dynamic loading and contact; contact model parameterisation, actuator model calibration • Real-time state estimation: floating-base EKF/UKF implementation and tuning for pelvis pose, velocity, and foot contact state at RT loop rates; feeds downstream controllers and loco-manipulation policy inputs • Sim-to-real pipeline: parameter estimation loops, hardware-data-driven calibration, validation against motion capture or external reference systems — the continuous feedback loop between hardware campaigns and updated sim models • Failure mode ownership: debugging model-accuracy-driven failures — control instability from inaccurate dynamics,…