Robotic digital fabrication is increasingly being integrated into architectural and construction practices, with extrusion-based robotic printing remaining one of the most widely adopted approaches. Most existing workflows rely on predefined geometries and offline toolpath planning, in which a complete motion script is generated in advance and then executed by the robot as a fixed sequence. While effective under controlled conditions, this static mode of fabrication limits the ability of the system to respond to geometric deviation, material instability, or process-based variation during printing. This research investigates a responsive robotic printing workflow based on point-to-point execution and open parametric control. Rather than sending a complete toolpath to the robot as a single predetermined script, the proposed system decomposes the printing trajectory into a sequence of discrete target points. Each point is transmitted and executed individually, allowing the system to receive, modify, and update fabrication parameters between consecutive robotic movements. Through the integration of Grasshopper, Python, and Machina, the workflow exposes control interfaces for position, orientation, speed, layer height, motion accuracy, and extrusion-related parameters, enabling these variables to enter the fabrication process during execution rather than only before printing begins. Within this framework, robotic printing is restructured from the passive execution of a fixed toolpath into a parameter-accessible fabrication process. External inputs, including manual parameter adjustment and sensor-derived data, can be introduced into the control pipeline and translated into updated robotic commands during point-to-point execution. This establishes a continuous relationship between parameter input, toolpath modification, and robotic motion, providing a technical basis for future sensor-data-informed and adaptive robotic fabrication workflows. In parallel, the research reconsiders the relationship between printable geometry, construction strategy, and structural formation. It focuses on arch-based and shell-based structures generated through continuous layered deposition. Inspired by Nubian vault construction principles, layer inclination is treated as a critical geometric and fabrication variable that directly affects deposition stability, interlayer contact, and structural buildability. Through a series of printing experiments with varying toolpath angles and geometric configurations, the study examines how initial contact conditions, layer-to-layer bonding, and deposition behavior influence the successful construction of self-supporting or minimally supported shell forms. A reusable sand formwork strategy is introduced as a temporary environmental support medium to stabilize the first printed layer and increase tolerance to variations in toolpath inclination. Experimental validation was conducted through both in-air and underwater robotic printing tests. The results demonstrate that arch-based shell printing is feasible when the initial contact condition is properly stabilized and the toolpath inclination remains within an appropriate range. Additional point-to-point control experiments further show that, once the printing trajectory is reorganized into individually executable motion units, the system can respond to parameter modifications during fabrication and apply updated values directly to subsequent robotic movements. Tests involving both manual parameter intervention and sensor-data input confirm that the proposed control framework can support real-time or pre-real-time updating of position, layer height, and other key fabrication parameters during the printing process. Taken together, this research demonstrates a robotic printing workflow based on point-to-point execution, open parametric interfaces, and process-level parameter accessibility. Its contribution is not the realization of a fully autonomous closed-loop control system, but the establishment of an intermediate control framework between conventional offline robotic fabrication and future sensor-driven adaptive construction. By linking geometric generation, parameter input, robotic execution, and process feedback, the study provides a technical and conceptual foundation for responsive robotic printing in uncertain fabrication contexts.