Prospective Perception

In this laboratory, we investigate the role of common sense in perception because we want to find out what prospective machinery efficiently drives our interpretation of the world to show how to overcome the high uncertainty resulting from the severe temporal, spatial and informational limitations in sensor data as the embodied agent interacts with its environment. Perception in such complex scenes (e.g., safety-critical, dynamic) does not only goes beyond processing sensor data to address classical tasks such as object classification, usually known as the what- and where-object-questions, but also faces the what-, how-, and why-happen-questions (e.g., task execution verification, estimation of physical parameters, quantities and detection of states such as fullness, stability). We generalize the problem of perception by placing events instead of objects at the center of scene understanding, where perception takes place as a loop which consists in predicting on the one hand the effects (anticipation) and on the other hand the causes (explanation) of events.