Locomotion Reinforcement Engineer

Key Responsibilities

Develop and implement reinforcement learning algorithms specialized for locomotion tasks (e.g., walking, running, climbing, balancing).

Design, integrate, and optimize high-fidelity simulation environments for safe and efficient policy training.

Conduct sim-to-real transfer by addressing robustness, domain randomization, and system identification challenges.

Incorporate perception, sensor feedback, and proprioception into RL agents to enable adaptive and reactive motion.

Evaluate and benchmark locomotion policies under diverse real-world conditions (e.g., terrain variation, disturbances, slopes, payloads, and friction).

Work on reward design, stability, sample efficiency, and safety-constrained learning.

Write clean, maintainable, and well-documented code, ensuring reproducibility and version control for experiments and policies.

Requirements

Solid background in Reinforcement Learning (Deep RL, Policy Gradient, Model-based RL, Imitation Learning, etc.).

Hands-on experience with simulation platforms such as MuJoCo, PyBullet, Isaac Gym, or Gazebo.

Preferred Qualifications

Experience with locomotion, motion control, or physical control systems (e.g., legged robots, drones, exoskeletons, robotic arms).

Experience in sim-to-real transfer, domain randomization, or system identification in robotics.

Proficiency in Python and/or C++, and familiarity with ML frameworks such as PyTorch, TensorFlow, or JAX.

Strong analytical and debugging skills for physical systems; ability to identify stability and performance bottlenecks.

Familiarity with sensor fusion, feedback control, and proprioceptive sensing.