Object-Centric Approach to Prediction and Labeling of Manipulation Tasks

We propose an object-centric framework to label and predict human manipulation actions from observations of the object trajectories in 3D space. The goal is to lift the low-level sensor observation to a context specific human vocabulary. The low-level visual sensory input from a depth camera is processed into high-level descriptive action labels using a directed action graph representation. It is built based on the concepts of pre-computed Location Areas (LA), regions within a scene where an action typically occur, and Sector-Maps (SM), reference trajectories between the LAs. The framework consists of two stages, an offline teaching phase for graph generation, and an online action recognition phase that maps the current observations to the generated graph. This graph representation allows the framework to predict the most probable action from the observed motion in real-time and to adapt its structure whenever a new LA appears. Furthermore, the descriptive action labels enable not only a better exchange of information between a human and a robot but they allow also the robots to perform high-level reasoning. We present experimental results on real human manipulation actions using a system designed with this framework to show the performance of prediction and labeling that can be achieved.