Responsible for developing and deploying learning-based control strategies for humanoid robot models, with a focus on reinforcement and imitation learning. The role centers on building robust training pipelines, designing diverse task environments, and managing experimentation workflows from simulation to real-world validation. It involves translating learning algorithms into reliable robot behaviors, addressing sim-to-real challenges, and ensuring performance through systematic benchmarking and analysis. Close collaboration with control, simulation, and hardware teams is required to align learned policies with physical system constraints and operational goals.
Responsibilities
- Design, implement and iterate RL/IL training pipelines for humanoid tasks in simulation and in the real world.
- Design and implement diverse task suites for manipulation, navigation, and whole-body coordination in simulation.
- Manage training experiments and evaluation loops: Hyperparameter tuning, Benchmarking, Logging and Failure analysis.
- Collaborate closely with multidisciplinary teams including: Motion and control engineers, Mechanical design team, Simulation engineers.
- Support sim-to-real

📌 Robotics Learning Engineer (Milano)
🏢 Oversonic Robotics
📍 Milano