Manipulation in a densely cluttered environment creates complex challenges in perception to close the control loop, many of which are due to the sophisticated physical interaction between the environment and the manipulator. Drawing from biological sensory-motor control, to handle the task in such a scenario, tactile sensing can be used to provide an additional dimension of the rich contact information from the interaction for decision making and action selection to manoeuvre towards a target. In this paper, a new tactile-based motion planning and control framework based on bioinspiration is proposed and developed for a robot manipulator to manoeuvre in a cluttered environment. An iterative two-stage machine learning approach is used in this framework: an autoencoder is used to extract important cues from tactile sensory readings while a reinforcement learning technique is used to generate optimal motion sequence to efficiently reach the given target. The framework is implemented on a KUKA LBR iiwa robot mounted with a SynTouch BioTac tactile sensor and tested with real-life experiments. The results show that the system is able to move the end-effector through the cluttered environment to reach the target effectively.