This thesis presents the design and control of the TActile Soft Quadruped (TASQ), a pneumatically actuated soft robot equipped with integrated tactile sensing for adaptive locomotion. Two core contributions are introduced. First, a novel tactile suction cup sensor is developed, capable of simultaneously providing foot contact information and generating suction-based adhesion. The sensor combines embedded magnets and magnetometers to estimate ground reaction forces via a learned calibration model, enabling lightweight, compliant, and robust tactile feedback essential for closed-loop control in soft robotics. Second, a learning-based control framework is proposed that integrates behavior cloning with domain-randomized reinforcement learning to achieve adaptive and robust locomotion. The approach first imitates a reference gait to initialize a stable walking policy and then refines it in simulation using the Soft Actor–Critic algorithm. The learned policy exploits proprioceptive and tactile feedback to enable goal-directed, stable motion and transfers effectively from simulation to real hardware. Experimental validation demonstrates that the learned closed-loop controller outperforms open-loop control on the physical robot, improving forward speed by 41\% on flat terrain and by 91\% on a $2.5^{\circ}$ incline. Ablation studies further confirm the importance of tactile and inertial feedback for stability and performance. Overall, this work establishes a unified sensing and learning framework for a soft legged robot, paving the way toward adaptive, environment-aware locomotion without reliance on vision.