Research Fellow – Modular Reconfigurable Mobile Robots
Core
Develop a reconfigurable control framework combining automatic robot modelling, adaptive switching control, and reinforcement learning for modular mobile robots.
Role type
Research Fellow (Robotics & Control)
Builds
MR² platforms capable of safe, efficient operation across varying configurations, modes, payloads, and environments.
Domain
Robotics, Control Engineering, Reinforcement Learning
Deliverable
production ML models
Required skills
robot modelling, adaptive control, switched systems, nonlinear control, nonholonomic mobile robots, deep reinforcement learning, sim-to-real transfer, Python, C, ROS 2, Linux, Git
Preferred skills
domain randomization, hierarchical learning, transfer learning, model-based RL, safe RL, real-time implementation, robot hardware integration
Technologies
ROS 2, Gazebo, NVIDIA Isaac Sim, Isaac Lab, MuJoCo, Python, C
Responsibilities
Design adaptive and switching control algorithms; design and train deep RL policies; develop scalable robot-training environments and simulation pipelines; implement sim-to-real methods; integrate learning-based policies with safety-critical control; deploy algorithms in simulation and real-world experiments.
