Biological structures engage in direct environmental interaction and acquire knowledge through multi sensory feedback from sensory input stimulants that influence the inner neuron structures that are formed. We describe a robotic system that uses multi sensory learning to handle things, taking influence from biological principles like the exploring and processing of sensory information, which ultimately lead to behavior learning. The robot can communicate smartly with its surrounding environment due to a small-scale biological neuromorphic prosthetic circuit that locally combines and interprets multi modal sensory input stimulants in an adaptable manner. Through the use of low-voltage organically neurological devices with a synapse capacity to handle sensory input stimulants in real-time, multi-sensory-associated linkages are formed, which eventually lead to behavioral training and the robot learning to avoid possibly hazardous things. A key component of the neuromorphic circuit's functionality is the employment of functioning components, such as organically semi-conducting compounds of polymer, which replicate bio-inspired features including dendritic summary, the plasticity of synapses, and neural computation. The monolithic polymeric electronics that are low-power, locally unified, and on a tiny scale can do this. Furthermore, the idea of handling sensory data of variable complexity and multifaceted communication can be expanded into several branches by virtue of the neuromorphic factor circuit's modular-like construction. This robotic device provides a concrete illustration of how localized organic neuromorphic factor circuits combined with bio-influenced principles might result in the creation of extremely adaptable, smart, and efficient systems for practical use.