For human-robot co-manipulation by robotic exoskeletons, the interaction forces provide a communication channel through which the human and the robot can coordinate their actions. In this article, an optimization approach for reshaping the physical interactive trajectory is presented in the co-manipulation tasks, which combines impedance control to enable the human to adjust both the desired and the actual trajectories of the robot. Different from previous studies, the proposed method significantly reshapes the desired trajectory during physical human-robot interaction (pHRI) based on force feedback, without requiring constant human guidance. The proposed scheme first formulates a quadratically constrained programming problem, which is then solved by neural dynamics optimization to obtain a smooth and minimal-energy trajectory similar to the natural human movement. Then, we propose an adaptive neural-network controller based on the barrier Lyapunov function (BLF), which enables the robot to handle the uncertain dynamics and the joint space constraints directly. To validate the proposed method, we perform experiments on the exoskeleton robot with human operators for co-manipulation tasks. The experimental results demonstrate that the proposed controller could complete the co-manipulation tasks effectively.