Integrating symbolic AI planning with robotic execution remains a central challenge in cognitive robotics and robotics education. While automated planning provides powerful tools for high-level reasoning, students often struggle to connect symbolic models with concrete robot behavior. This pa-per presents the design, implementation, and evaluation of an educational workshop that bridges AI-based task planning and robotic manipulation through simulation in NVIDIA Isaac Sim. The workshop was developed as part of an advanced Cognitive Robotics course and combines symbolic plan-ning using the Unified Planning Framework (UPF) and PDDL with execu-tion of manipulation plans by a simulated Franka Emika Panda robotic arm. A Robot Manipulation Language (RML) was introduced to abstract gripper orientations and model pick-and-place actions symbolically, enabling stu-dents to reason about spatial affordances while maintaining executability. The workshop follows a two-stage workflow: modeling and planning manip-ulation tasks, followed by execution and validation in a high-fidelity simula-tion environment. Evaluation results based on student performance, reports, and questionnaires indicate that the workshop effectively supported under-standing of AI planning, robot affordances, and the planning–execution loop. The study demonstrates the educational potential of modern robotic simulation tools for making complex cognitive robotics concepts accessible and actionable.