The field of robotics is witnessing significant developments in control and safety mechanisms. Researchers are exploring innovative approaches to improve the stability, adaptability, and reliability of robotic systems. A key direction is the integration of model predictive control (MPC) and machine learning techniques to enable robots to adapt to changing environments and unexpected disturbances. Another area of focus is the development of safety frameworks that can ensure collision avoidance and maintain safe interaction with humans and the environment. Noteworthy papers in this area include: Mass-Adaptive Admittance Control for Robotic Manipulators, which presents a novel approach to handling objects with unknown masses. Geometric Formulation of Unified Force-Impedance Control on SE(3) for Robotic Manipulators, which enables force tracking while guaranteeing passivity. Learning Verifiable Control Policies Using Relaxed Verification, which proposes a method for verifying control policies throughout training to ensure safety guarantees.