Mohamed bin Zayed University of Artificial Intelligence
This course introduces the theoretical foundations and practical algorithms for motion planning in robotics. Topics include configuration spaces, graph search methods, sampling-based planning, planning under uncertainty, and symbolic/task planning. Students learn how to design planners with correctness and optimality guarantees and implement them in modern robotics frameworks.
This course explores how robot hands manipulate, sense, and learn from in-hand interactions via model-based control, tactile perception, and data-driven learning. Students program goal-directed, highly dynamic dexterous skills by integrating motion/force control, reinforcement learning, and sim-to-real deployment.
Students implement and test model-based and learning-based in-hand manipulation on a physics simulator and a dexterous hand platform using tactile and visual feedback. Activities include building goal-conditioned controllers and RL policies, sim-to-real transfer, benchmarking on manipulation tasks, and analyzing performance, safety, and robustness.