Abstract
Robots are most widely used to replace human contribution with machine generated response. When humans interact with robots, its mandatory for both to forecast actions based on current conditions. Huge efforts have been channelized towards attaining this perfect coordination. To decipher complex environments, the inference of robotic mobility and alteration of random unstructured scenarios is a complicated task in the field of visual processing and imaging. To address this issue, a new Vision-Based Interaction Model based on deep neural networks has been suggested. The proposed model solves the error amplification issue by the application of past inputs through features as reposed by a Deep Belief Network (DBN). In addition, a novel Vision-Based Robotics Learning model is also proposed for scene understanding and recognition using deep neural network understanding. Moreover, a vision theory-based smart learning algorithm is also suggested to decide positive possible outcomes.Therefore, the model is capable of using object motions to extract relevant information used for Turning, Griping and object mobility.To validate the suggested model, a number of experiments have been performed on benchmark datasets and it showed a higher performance as evaluated against some of the niche methods.