Nociception is a protective biological mechanism that links harmful stimulation to a reaction, such as rapid withdrawal. This paper investigates artificial nociception for a collaborative robotic arm with whole-body tactile sensing. We present a complete pipeline that maps pressure changes from sensitive skin on a robot manipulator to bio-inspired withdrawal motions. The system first converts skin pressure into a scalar pain gain using a nonlinear continuous model. We compare three reflexes: (i) uniform reflex moves four robot joints by a fixed amount, whereby the withdrawal is approximated by a stereotypical movement of the arm "toward the base", independent of where the robot was touched; (ii) biologically motivated location-dependent joint-space withdrawal derived from human withdrawal reflex characteristics; (iii) Cartesian space withdrawal along the surface normal of the contacted skin pad. All behaviors are integrated in a finite-state reflex controller that interrupts the task, executes the withdrawal, and returns to a pre-contact pose. A user study with 15 participants compared the three strategies using Godspeed questionnaire subscales, custom perceived-naturalness and safety items, forced-choice comparisons, and qualitative feedback. Interestingly, participants better rated the stereotypical uniform reflex behavior over one or both competitors on the anthropomorphism, animacy, and likeability Godspeed subscales and on the Naturalness and Realism custom scale. When asked to compare the conditions, the uniform reflex was scored best in "felt safest", "most human-like", and "most natural". This suggests that predictability of the robot behavior is key for user acceptance. The Cartesian reflex was judged the most appropriate reaction to touch. The bio-inspired reflex did not score best in either score. This may be partly attributed to the embodiment gap between the robot arm and human arm and participants having different expectations from a robot manipulator.
