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

Neura Robotics; Gemeindeverwaltungsverband Metzingen, Riederich; Vor Ort; EUR 90.000 - 130.000; Vollzeit

Zusammenfassung

Neura Robotics is seeking a seasoned researcher to own dynamic model fidelity and real-hardware calibration for a legged/mobile humanoid platform. You will design excitation trajectories, perform regression fits, and refine estimators to ensure accurate pelvis pose, velocity, and contact state estimation in real time.

The role emphasizes integrating Pinocchio, MuJoCo, and differentiable simulators with MPC/WBC or learned policies, while translating models into robust production code in C++ and

Qualifikationen

• MSc or PhD in Robotics, Mechanical Engineering, Electrical Engineering or related field.
• 4+ years of experience developing state estimation or system identification for real-time robotic systems on hardware.
• Experience identifying parameters on physical robots: excitation trajectories, regression fitting, and parameter sensitivity.

Aufgaben

• Ownership of dynamic model fidelity and real-hardware matching across motion and contact interactions.
• Perform system identification on 4NE1 hardware: motor constants, friction, transmission dynamics.

Kenntnisse

State estimation

System identification

Dynamics & control

Real-time robotics

C++

Python

Control systems

Ausbildung

MSc or PhD in Robotics or related field

Tools

Pinocchio

MuJoCo

GTSAM/iSAM2

Differentiable simulators (Brax, DiffTaichi)

Jobbeschreibung

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, 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

What we can look forward to

• 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

Nice to Have

• Humanoid or legged robot hands-on experience — 4NE1 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