Object reorientation is a critical task for robotic grippers, especially when manipulating objects within constrained environments. The task poses significant challenges for motion planning due to the high-dimensional output actions with the complex input information, including unknown object properties and nonlinear contact forces. Traditional approaches simplify the problem by reducing degrees of freedom, limiting contact forms, or acquiring environment/object information in advance, which significantly compromises adaptability. To address these challenges, we deconstruct the complex output actions into three fundamental types based on tactile sensing: task-oriented actions, constraint-oriented actions, and coordinating actions. These actions are then optimized online using gradient optimization to enhance adaptability. Key contributions include simplifying contact state perception, decomposing complex gripper actions, and enabling online action optimization for handling unknown objects or environmental constraints. Experimental results demonstrate that the proposed method is effective across a range of everyday objects, regardless of environmental contact. Additionally, the method exhibits robust performance even in the presence of unknown contacts and nonlinear external disturbances.