"Robotic micromanipulation is a widely used experimental technique to physically interact with a microscale sample under a microscope, and has found important applications in cell microinjection, mechanical characterization of biomaterials, and assembly of micro-parts. Force sensing and control play important roles in robotic micromanipulation. For instance, monitoring the contact force in cell injection indicates the moment of cell membrane penetration, and controlling the grasping force of a microgripper allows accurate characterization of the mechanical properties of a material being manipulated. Recently, force-controlled robotic micromanipulation has found its new application in studying the neuron-level mechanobiology of Drosophila larvae. Specifically, the capability of accurately applying millinewton-level touch stimuli to a Drosophila larva and simultaneously observing resultant fluorescence signal transmissions in its mechanosensitive neurons will enable novel research on mechanisms of the animal's mechanotransductive neural circuitries. The conventional method is to conduct the experiment manually, which is time consuming and requires extensive training for operators. A robotic micromanipulation system designed for this type of experiments could greatly facilitate the mechanobiology research on Drosophila with much higher accuracy, efficiency, and repeatability.In this thesis, a force-controlled robotic micromanipulation system is developed for simultaneously applying accurate mechanical stimuli and quantifying fluorescence neuron transmission signals in Drosophila larvae. The system employs an elastomeric microdevice for immobilizing individual larvae on a substrate, and a microelectromechanical systems (MEMS) piezoresistive force sensor for applying a closed-loop controlled touch stimulus to a larva. A micromanipulator and a microscope XY stage are coordinately servoed using orchestrated position and force control laws for automatic operations. The system performs force-controlled larva touching and fluorescence imaging at a speed of four larvae per minute, with a success rate of 92.5%. A new force control architecture, including a compensation-prediction scheme and a switched fuzzy to proportional-integral-derivative (fuzzy-PID) controller, is also proposed to effectively improve the dynamics of the force control system. The compensation-prediction scheme is employed to accommodate force measurement noise, system modelling errors, time delays and lack of position feedback from the micromanipulator. The switched fuzzy-PID controller is proposed to ensure the fast convergence and small steady-state oscillation of the system. Compared to conventional PID control scheme, the proposed architecture reduces the force overshoot to