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Client:
Humanoid
Location:
London, United Kingdom
Job Category:
Other
EU work permit required:
Yes
Job Views:
3
Posted:
24.04.2025
Expiry Date:
08.06.2025
Job Description:
Humanoid is the first AI and robotics company in the UK, creating the world’s most advanced, reliable, commercially scalable, and safe humanoid robots. Our first humanoid robot HMND 01 is a next-gen labour automation unit, providing highly efficient services across various use cases, starting with industrial applications.
We’re seeking a highly skilled Staff Control Engineer to take ownership of the locomotion control systems for our biped humanoid robots. You’ll be at the cutting edge of robotics, responsible for developing advanced control algorithms that balance precision, efficiency, and adaptability. This role focuses on designing robust controllers for walking, balancing while manipulating, fall recovery, and other advanced mobility tasks. The ideal candidate will have strong expertise in classic locomotion pipeline, whole-body control and reinforcement learning.
Key Responsibilities:
* Design and implement control algorithms (classic or RL-based policies) for locomotion tasks, including walking, balancing while manipulating, squatting, stair climbing, fall recovery, and other dynamic maneuvers.
* Model and simulate complex dynamics, taking into account robot kinematics, actuator limitations, and environmental interactions to optimize performance.
* Conduct rigorous testing in both simulated and real-world environments to validate algorithms and ensure robustness across various conditions.
* Work closely with software, hardware, and perception teams to integrate control strategies into the overall robotic system.
Required Qualifications:
* Master’s or PhD in Robotics, Control Systems, Mechatronics, or a related field.
* At least 5 years of experience in the design and implementation of control systems for biped robots, focusing on locomotion.
* Proficiency with model predictive control (MPC), optimal control, and feedback control loops in dynamic robotic systems.
* Expertise in reinforcement learning for robotics.
* Deep understanding of humanoid robot dynamics and balance control.
* Strong experience with hardware-in-the-loop testing and deployment on physical humanoid robots.
* Strong hands-on experience with robot simulation platforms such as Mujoco, Isaac Sim or similar environments.
* Proficiency in Python and C++ for algorithm development, testing, and deployment.
* Knowledge of advanced topics like model-free RL, imitation learning, or hybrid control systems that combine classic and modern methods.
* Familiarity with real-time control systems and integration with hardware, including actuators and sensors.
* Expertise in sensor fusion for state estimation (IMUs, joint encoders, force/torque sensors) to enable robust balance and locomotion control.
Preferred Qualifications:
* Understanding of actuators dynamics and modeling.
* Deep knowledge of dynamics and control of legged robots, including whole-body control, balance, and stability management (ZMP, capture point control, etc.).
* Experience designing control algorithms for actuator-level performance, including torque control, impedance/admittance control, position/velocity control, and force control.
* Familiarity with series elastic actuators (SEAs), harmonic drives, direct drive motors, and custom actuator designs used in humanoid robotics.
* Proven experience in robotic system integration, including end-to-end validation of locomotion systems (joint to whole-body motion).
* Design and execution of hardware-in-the-loop (HIL) and software-in-the-loop (SIL) simulations for testing control algorithms.
* Skilled in developing automated test frameworks to validate actuator performance (torque tracking, bandwidth, latency, etc.) and locomotion stability across various terrains and unexpected disturbances.
* Experience with failure mode analysis, robustness testing, and system identification for actuators and control loops.
* Ability to conduct experimental validation and tuning of controllers on full-scale humanoid platforms, including lab and field testing.
* Familiar with compliance testing, gait optimization, and energy efficiency evaluation for legged robots.
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