Humans have an incredible sense of self-preservation that is both instilled, and also learned through experience. One system which contributes to this is the pain and reflex system which both minimizes damage through involuntary reflex ac- tions and also serves as a means of ‘negative reinforcement’ to allow learning of poor actions or decision. Equipping robots with a reflex system and parallel learning architecture could help to prolong their useful life and allow for continued learning of safe actions. Focusing on a specific mock-up scenario of cubes on a ‘stove’ like setup, we investigate the hardware and learning approaches for a robotic manipulator to learn the presence of ‘hot’ objects and its contextual relationship to the environment. By creating a reflex arc using analog electronics that bypasses the ‘brain’ of the system we show an increase in the speed of release by at least two-fold. In parallel we have a learning procedure which combines visual information of the scene with this ‘pain signal’ to learn and predict when an object may be hot, utilizing an object detection neural network. Finally, we are able to extract the learned contextual information of the environment by introducing a method inspired by ‘thought experiments’ to generate heatmaps that indicate the probability of the environment being hot.