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

Your Mission \& Challenges
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• 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 4NE\-1 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, estimation drift or bias causing divergence, incorrect contact/force estimation leading to instability in dynamic interactions


• Cross\-team interface: supplying updated Pinocchio model parameters to the WBC and State Estimation Engineers in Core Robot Software; aligning on excitation trajectory designs with the Locomotion and RL/Control Engineers

• MSc or PhD in Robotics, Mechanical Engineering, Electrical Engineering, or a related field with a strong foundation in dynamics, estimation, and control


• 4\+ years of experience developing state estimation or system identification solutions for real\-time robotic systems — on real hardware, not simulation\-only


• System identification on physical robotic systems: excitation trajectory design, least\-squares or maximum\-likelihood regressor fitting, actuator and transmission parameter identification


• State estimation implementation: EKF or UKF for floating\-base pose, velocity, and contact state on a legged or mobile robot platform


• Rigid body dynamics depth: contact modelling, actuator behaviour, and how model inaccuracies propagate to control instability — not just theoretical familiarity


• Experience supporting control systems (MPC, WBC) or learned policies (RL) through hardware deployment — understanding how model quality gates policy transfer


• C\+\+ for production RT systems; Python for analysis, tooling, and calibration pipelines

• Humanoid or legged robot hands\-on experience — 4NE\-1 is a full\-size humanoid; bipedal dynamics and contact complexity are directly relevant


• Differentiable simulators for gradient\-based system identification (Brax, DiffTaichi, or comparable)


• Sim\-to\-real transfer methodology: domain randomisation, adaptive calibration, residual physics modelling


• Pinocchio for rigid\-body model computation and parameter sensitivity analysis


• MuJoCo model authoring: MJCF contact parameters, actuator models, tendon dynamics


• Factor graph\-based estimation (GTSAM, iSAM2\) for tightly\-coupled IMU \+ kinematics fusion


• Publications or open\-source contributions in legged robot dynamics, system identification, or sim\-to\-real transfer

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