This week explores how robots perceive their state and environment, and how they physically interact with it. The first part focuses on sensing and estimation, covering proprioceptive methods such as odometry and inertial navigation, as well as exteroceptive vision-based techniques including camera modeling, calibration, triangulation, and 3D perception through point cloud registration. The second part addresses force control and compliant interaction, introducing impedance and admittance control, multi-degree-of-freedom systems, and hybrid force/motion control strategies for constrained tasks such as peg-in-hole operations. Through these topics, learners gain a unified view of perception and interaction as tightly coupled processes in robotic systems.

This week explores how knowledge representation and reasoning (KRR) enable adaptive and goal-oriented robotic behavior. Learners examine how high-level knowledge is translated into executable actions, including planning as a search process, formal representation languages, and knowledge-based control architectures. The role of digital twins is introduced as a powerful framework for integrating knowledge, simulation, and real-world interaction. The week concludes with wheeled robots, analyzing mobility principles, design choices, and their use in real-world applications. Learners explore how decision-making and motion are integrated in autonomous systems.

This week focuses on robotic manipulation, starting from the fundamental problem of grasping and its mathematical modeling. Learners explore grasp representations, friction constraints, and key tools such as the grasp matrix and hand Jacobian, extending to advanced concepts including compliance and postural synergies in robotic hands. The second part introduces cooperative manipulators, highlighting the motivations, modeling approaches, and control strategies for multi- robot manipulation. The week provides both theoretical foundations and insights into recent developments in coordinated manipulation.