The field of robotics and 3D printing is experiencing significant advancements in motion planning and trajectory optimization. Researchers are exploring new methods to improve the efficiency and accuracy of robotic manipulators, including the use of configuration space distance fields and model predictive path integral control. These approaches enable direct navigation in the robot's configuration space, reducing computation time and preserving collision avoidance. Additionally, novel techniques for generating ruled surfaces and toolpath optimization are being developed for linear hot-wire rough machining and multi-axis 3D printing. Noteworthy papers include: One-Step Model Predictive Path Integral for Manipulator Motion Planning Using Configuration Space Distance Fields, which achieves nearly 100% success rates in 2D environments and high success rates in 7-DOF Franka manipulator simulations. INF-3DP: Implicit Neural Fields for Collision-Free Multi-Axis 3D Printing, which unifies toolpath generation and global collision-free motion planning, achieving up to two orders of magnitude speedup compared to explicit-representation-based methods.