Human-Robot Interaction Optimization Through Behavioural Data Analysis and Sequential Deep Models

In this work, we investigate and model HumanRobot Interaction (HRI) optimization considering behavior data based on a set of sequential deep learning models. We consider the application of recurrent models, including LSTMs and RNNs, to fidelity in prediction and adaptation to human activities such as speech, gestures, and locomotion. The work is focused on the incorporation of uncertainty management and Bayesian models so that the noisy or ambiguous human input can be handled and robots will be addressed in a more precise way to react in a dynamic environment. Simulation experiments are performed to evaluate the robot’s prediction accuracy, adaptability and task success in various human interaction situations. Results show the improvement of the robot performance through time with improved accuracy in personalized responses and task execution. The work reveals the promise of deep learning models for enhancing the trustworthiness and resilience of human-robot collaboration, especially in authentic and unpredictable environments.