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Manipulation in tight environment is challenging but increasingly common in vision-guided robotic applications. The significantly reduced amount of available feedback (limited visual cues, field of view, robot motion space, etc.) hinders solving the hand-eye relationship accurately. In this article, we propose a new generic approach for online robot-camera calibration that could deal with the least feedback input available in tight environment: an arbitrarily restricted motion space and a single feature point with unknown position for the robot end-effector. We introduce the interactive perception to generate prescribed but tunable robot motions to reveal high-dimensional sensory feedback, which is not obtainable from static images. We then define the interactive feature plane (IFP), whose spatial property corresponds to the robot-actuating trajectories. A depth-free adaptive controller is proposed based on image feedback, where the converged orientation of IFP directly harvests the data for solving the hand-eye relationship. Our algorithm requires neither external calibration sensors/objects nor large-scale data acquisition process. Simulations demonstrate the validity of our method to accurately calibrate different types of robot under various system set-ups. In experiments, we show good results of our algorithm in terms of accuracy and consistency under tight motion space compared to existing approaches using external objects and/or optimization.