As a result, social behavior is one of the most sought-after qualities that a robot can possess.
However, it is not trivial to execute the human intended tasks as natural language expressions can have large linguistic variations.
Our proposed pipeline consists of -- a) a skeletal key points estimator (a total of 17) for the detected human in the scene, b) a learning model (using a feature vector based on the skeletal points) using CRF to detect groups of people and outlier person in a scene, and c) a separate learning model using a multi-class Support Vector Machine (SVM) to predict the exact F-formation of the group of people in the current scene and the angle of approach for the viewing robot.
In this work, we provide a non-trivial method to combine an NLP engine and a planner such that a robot can successfully identify tasks and all the relevant parameters and generate an accurate plan for the task.
In this work, we focus on a telepresence robot that can be used to attend a meeting remotely with a group of people.