Classical Foundations
The kinematics, planning, control and estimation stack that sits under every learned policy.
This domain overview lists 9 articles, ordered as a dependency chain rather than a survey. Kinematics is assumed by planning and planning by control. Each entry leans on whatever came before it, so the last two rest on the whole chain.
Forward and inverse kinematics, DH parameters, and the Jacobian; the theory behind the 3D playground.
RRT and its optimal variants, trajectory optimization, and CHOMP/TrajOpt.
PID, LQR, MPC, and whole-body QP: the classical stack under every learned policy.
Kalman filters, factor graphs, and pose estimation from noisy sensors.
Contact mechanics, grasp quality metrics, and force closure.
Calibration through 6-DoF pose: the pipeline that finds the object, and its error budget.
What a robot remembers about the space around it, and why the map that renders best is not the map a planner can use.
Camera intrinsics, hand-eye transforms, kinematic zeroes, timing and dynamics: the measurements that make sensor coordinates agree with motion.
Topics, services, actions, QoS, tf2, rosbag2 and MoveIt explained as the production boundary around a learned policy.