The work focuses on the development of an adaptive technique for the physical interaction handling between a human and a robot, as well as its experimental validation. The proposed technique is based on the deep residual neural network and dedicated finite state machine, where the states are the robot behavior modes and transitions are the switchings between the states that depend on the interaction parameters and characteristics. It ensures the human operator safety and improves the human–robot collaboration performance by implementing various scenarios. In the scope of this technique, the parameters of human–robot interaction are used to select an appropriate robot reaction strategy using data from internal robot sensors only, i.e. proprioceptive sensors. These parameters define the interaction force vector and its application point on the robot surface, which allow to classify the interaction within the set of predefined categories. This classification distinguishes interactions applied at the tool or intermediate link (Tool/Link), having soft or hard nature (Soft/Hard), as well as having different intention (Intl/Accd) or duration (Short/Long). Based on identified category and the current robot state, the algorithm chooses an appropriate robot reaction. To confirm the efficiency the developed technique, an experimental study was conducted, which involved the collaboration between the real industrial manipulator KUKA LBR iiwa and the human operator.