Locomotion & Whole-Body Control Engineer (human)

NEURA Robotics; Germany (OH); On-site; USD 90,000 - 140,000; Full time

Job summary

NEURA Robotics is seeking a senior robotics engineer to advance legged locomotion through trajectory optimization, MPC, and whole-body control on real hardware. You will work with state estimation, simulation, and hardware teams to ensure robust, real-time performance on floating-base platforms.

The role requires hands-on experience with MPC, WBC, and QP-based control at high frequencies, plus strong C++ and Python skills.

Qualifications

• MSc or PhD in robotics, controls, mechanical or electrical engineering, computer science, or related field.
• 4+ years hands-on experience developing trajectory optimization, MPC for locomotion, and/or whole-body control on physical robots.
• Strong foundation in floating-base articulated rigid-body dynamics and contact modelling.
• Strong knowledge of reduced-order locomotion models (LIPM, SRBD, centroidal dynamics) and their use inside MPC.
• Strong foundation in optimal control, constrained numerical optimization, and model-predictive control for legged robots.
• Hands-on experience with whole-body QP / TSID frameworks on real robot data with QP / DDP solver internals.
• Experience deploying real-time control/MPC/WBC pipelines at 500 Hz – 1 kHz on hardware.
• Strong C++ for real-time robotics software; Python for analysis and tooling.

Responsibilities

• Whole-body motion generation and control for floating-base legged platforms, including locomotion, balance, and contact transitions.
• Trajectory optimization and MPC pipelines over state, contacts, ground reaction forces, and centroidal momentum.
• QP-based task-space inverse dynamics to execute MPC/trajectory-optimization references at high frequency.
• Whole-body modelling from URDF/MJCF, including FK/IK, Jacobians, and dynamics calculations.
• Define and manage constraints across MPC and QP layers with task hierarchy for the platform.
• Improve solver performance, warm-starting, conditioning, and reliability at high update rates.
• Deploy, tune, and debug MPC, trajectory optimization, IK, and inverse dynamics on physical robots.
• Calibrate contact models and validate against real robot data; emphasize robust, predictable locomotion.

Skills

Trajectory optimization

MPC for locomotion

Whole-body control

Floating-base dynamics

Contact modelling

Real-time robotics software

C++ for robotics

Python tooling

Education

MSc/PhD in robotics, controls, or related field

Tools

Pinocchio

MuJoCo

Crocoddyl

IPOPT

TSID

OCS2

Job description

Your mission & challenges

We are growing our legged-robotics capability on NEURA's humanoid (4NE-1) and quadruped platforms. This role spans both core layers of the legged control stack: trajectory optimization and MPC for kino-dynamic motion generation, and QP-based instantaneous whole-body control for executing those motions on real hardware at 1 kHz.

The work is focused on contact-rich dynamics, real-time optimization, and reliable execution on physical robots. You will collaborate closely with state estimation, simulation, low-level control, and hardware stakeholders, and with the application teams whose tasks ultimately depend on robust, predictable locomotion and whole-body behaviour.

• Whole-body motion generation and control for floating-base legged platforms — locomotion, balance, contact transitions, and loco-manipulation (walking while manipulating).
• Trajectory optimization and model-predictive control pipelines over robot state, contact schedules, ground reaction forces, centroidal momentum, and joint trajectories — using reduced-order locomotion models such as LIPM, SRBD, and centroidal dynamics.
• QP-based task-space inverse dynamics for executing instantaneous whole-body control from MPC and trajectory-optimization references at 1 kHz on the real robot.
• Whole-body modelling for the platform: floating-base rigid-body dynamics from URDF / MJCF, joint configuration, FK / IK, Jacobians, and mass / Coriolis / gravity computation.
• Constraint formulation across the MPC and QP layers — contact, friction, torque, joint, kinematic, and stability constraints — with task-hierarchy design appropriate to the platform.
• Solver performance work across both layers: warm-starting, numerical conditioning, constraint handling, and real-time reliability at 500 Hz – 1 kHz.
• Deployment, tuning, and debugging of MPC, trajectory optimization, IK, and inverse dynamics pipelines on physical robots — including platform-specific contact-model calibration and validation against real robot data.
• High-performance C++ for real-time execution; Python tooling for analysis, prototyping, and debugging.

What we can look forward to

• MSc or PhD in robotics, controls, mechanical or electrical engineering, computer science, or a related field.
• 4+ years of hands‑on experience developing trajectory optimization, MPC for locomotion, and / or whole-body control on physical robots.
• Strong foundation in floating‑base articulated rigid‑body dynamics and contact modelling.
• Strong working knowledge of reduced‑order locomotion models (LIPM, SRBD, centroidal dynamics, or equivalents) and their use inside MPC.
• Strong foundation in optimal control, constrained numerical optimization, and model‑predictive control for legged robots.
• Hands‑on experience with whole‑body QP / TSID frameworks on real robot data — including QP / DDP solver internals.
• Hands‑on experience deploying real‑time control / MPC / WBC pipelines at 500 Hz – 1 kHz on hardware.
• Strong C++ for real‑time robotics software; Python for analysis, tooling, prototyping, and debugging.
• Practical understanding of how contact dynamics, actuator limits, latency, state‑estimation error, solver failure modes, and model mismatch behave on real hardware.
• A collaborative working style: shared design, constructive code review, proactive communication, and reliable coordination across control, estimation, simulation, low‑level control, and hardware disciplines. Strong teamwork is essential for this role.

Nice to Have

• Hands‑on experience on humanoids, quadrupeds, or other high-DOF legged robots.
• Familiarity with Pinocchio, MuJoCo, Crocoddyl, IPOPT, TSID, OCS2, or similar open-source tools.
• Hierarchical QP, weighted QP, task prioritization, contact force optimization, or operational-space control.
• Contact planning, gait optimization, balance recovery; CPG-based or hybrid CPG / MPC controllers.
• Multi-contact WBC: foot contact, bimanual grasping, or base‑arm coordination.
• Contact-consistent dynamics and impact‑aware control transitions.
• Experience with torque‑controlled robots and high‑bandwidth electric actuation.
• Publications at RSS, ICRA, IROS, or CoRL in legged locomotion or whole-body control.