With Industry 4.0 becoming a reality and Industry 5.0 emerging on the horizon, the need for seamless integration, shared workspace and interoperability of production entities is ever increasing. To aid in this transition, this thesis presents approaches intended to allow the evolution of industrial robots by enabling them to detect and interpret interactions with their surroundings. The detection of interaction forces is based on non-model-based algorithms due to their inherent ability to include all aspects of the behaviours of the robot as well as to capture the contact task-specific forces and dynamics. To detect interactions, the reference sequence recorded during an exemplary task execution cycle is compared with measurements from the robot while it is performing its repetitive task. The thesis presents several different approaches to detection of collisions and interactions in general intended for the implementation on industrial robots with closed control architecture. To overcome implementation issues, the modified Dynamic Time Warping (mDTW) method, as one of the key presented contributions, enables optimal matching of compared signals. The mDTW enables comparing a signal with the most similar section of the other signal. Partial matching also enables online application of time warping principles and reduces the time and computation resources needed to perform matching. The developed and presented algorithms for automatic calculation of kinematic parameters of the robot and its end-effector enable further evolution of the mDTW in into its kinematically augmented version-KA-mDTW, extending the interaction’s detection algorithm’s application domain. Furthermore, it enables the inclusion of unmodeled task dynamics or a robot’s endeffector into algorithms for collision detection or general understanding of a robot’s operation context. The presented algorithms and conclusions are supported and validated by the experimental testing on industrial robots.